From 9b24b7176e59f81c59da80c99f1c1907c19caadf Mon Sep 17 00:00:00 2001 From: Fantasy-Dzx <1437749196@qq.com> Date: Tue, 3 Sep 2024 09:30:32 +0800 Subject: [PATCH] update --- PlatEMO/Algorithms/Blocks/BLOCK.p | Bin 0 -> 1290 bytes PlatEMO/Algorithms/Blocks/BlockEA.m | 35 + PlatEMO/Algorithms/Blocks/Block_Crossover.m | 13 + PlatEMO/Algorithms/Blocks/Block_Crossover.p | Bin 0 -> 759 bytes PlatEMO/Algorithms/Blocks/Block_Exchange.m | 12 + PlatEMO/Algorithms/Blocks/Block_Exchange.p | Bin 0 -> 458 bytes PlatEMO/Algorithms/Blocks/Block_Kopt.m | 12 + PlatEMO/Algorithms/Blocks/Block_Kopt.p | Bin 0 -> 520 bytes PlatEMO/Algorithms/Blocks/Block_Mutation.m | 12 + PlatEMO/Algorithms/Blocks/Block_Mutation.p | Bin 0 -> 586 bytes PlatEMO/Algorithms/Blocks/Block_Population.m | 11 + PlatEMO/Algorithms/Blocks/Block_Population.p | Bin 0 -> 206 bytes PlatEMO/Algorithms/Blocks/Block_Selection.m | 12 + PlatEMO/Algorithms/Blocks/Block_Selection.p | Bin 0 -> 682 bytes PlatEMO/Algorithms/Blocks/Block_Tournament.m | 13 + PlatEMO/Algorithms/Blocks/Block_Tournament.p | Bin 0 -> 471 bytes .../AC-MMEA/ACMMEA.m | 121 ++ .../AC-MMEA/CalFitness.m | 47 + .../AC-MMEA/Clustering.m | 70 + .../AC-MMEA/CpuGroup.m | 34 + .../AC-MMEA/EnvironmentalSelection.m | 45 + .../AC-MMEA/EnvironmentalSelectionS.m | 66 + .../AC-MMEA/Operator.m | 63 + .../AC-MMEA/OperatorGAhalfSMM.m | 202 +++ .../AC-MMEA/SubPopRank.m | 30 + .../AC-MMEA/SubPopSimility.m | 85 ++ .../AC-MMEA/UpdateGV.m | 32 + .../Multi-objective optimization/CMMO/CMMO.m | 2 +- .../LDS-AF/LDSAF.m | 98 ++ .../LDS-AF/RBFN_P.m | 85 ++ .../LDS-AF/RBFN_PP.m | 84 ++ .../LDS-AF/SolutionSelection.m | 23 + .../LMEA/VariableClustering.m | 6 +- .../LRMOEA/ArchUpdate.m | 59 + .../LRMOEA/Construction.m | 66 + .../LRMOEA/EnvironmentalSelection.m | 38 + .../LRMOEA/Final.m | 47 + .../LRMOEA/FitnessCal.m | 30 + .../LRMOEA/Initialization.m | 18 + .../LRMOEA/LRMOEA.m | 57 + .../LRMOEA/LROperator.m | 62 + .../LRMOEA/archives.m | 55 + .../MOBCA/MOBCA.m | 2 +- .../MOEA-CKF/BinaryCrossover.m | 24 + .../MOEA-CKF/BinaryMutation.m | 23 + .../MOEA-CKF/EnvironmentalSelection.m | 63 + .../MOEA-CKF/MOEACKF.m | 79 ++ .../MOEA-CKF/PriorAnalysis_initialization.m | 44 + .../MOEA-CKF/Reproduction1.m | 145 +++ .../MOEA-CKF/Reproduction2.m | 71 + .../MOEA-CKF/SparsityAnalysis.m | 40 + .../MOEA-NZD/DimJud.m | 55 + .../MOEA-NZD/DimJud0.m | 47 + .../MOEA-NZD/EnvironmentalSelection.m | 88 ++ .../MOEA-NZD/ExactKmeans.m | 46 + .../MOEA-NZD/GA0.m | 142 ++ .../MOEA-NZD/MOEANZD.m | 71 + .../NSBiDiCo/EnvironmentalSelection.m | 31 + .../NSBiDiCo/MatingSelection.m | 52 + .../NSBiDiCo/NSBiDiCo.m | 39 + .../NSBiDiCo/UpdateArc.m | 68 + .../SSDE/Operator.m | 60 + .../Multi-objective optimization/SSDE/SSDE.m | 61 + .../SSDE/Training.m | 74 ++ .../TELSO/EnvironmentalSelection.m | 59 + .../TELSO/Initial_EnvironmentalSelection.m | 32 + .../TELSO/Operator.m | 56 + .../TELSO/TELSO.m | 88 ++ PlatEMO/GUI/Draw.m | 4 +- PlatEMO/GUI/GUI.m | 118 +- PlatEMO/GUI/GUI.mat | Bin 421663 -> 345727 bytes PlatEMO/GUI/module_app.m | 253 ++-- PlatEMO/GUI/module_cre.m | 1156 ++++++++++++++++- PlatEMO/GUI/module_exp.m | 142 +- PlatEMO/GUI/module_test.m | 218 +--- PlatEMO/GUI/uicontext.m | 16 +- PlatEMO/GUI/uilist.m | 25 +- .../Multi-objective optimization/FDA/FDA2.m | 4 +- .../GLSMOP/GLSMOP1.m | 87 ++ .../GLSMOP/GLSMOP2.m | 91 ++ .../GLSMOP/GLSMOP3.m | 91 ++ .../GLSMOP/GLSMOP4.m | 91 ++ .../GLSMOP/GLSMOP5.m | 88 ++ .../GLSMOP/GLSMOP6.m | 92 ++ .../GLSMOP/GLSMOP7.m | 92 ++ .../GLSMOP/GLSMOP8.m | 92 ++ .../GLSMOP/GLSMOP9.m | 104 ++ .../LRMOP/LRMOP1.m | 83 ++ .../LRMOP/LRMOP2.m | 92 ++ .../LRMOP/LRMOP3.m | 92 ++ .../LRMOP/LRMOP4.m | 94 ++ .../LRMOP/LRMOP5.m | 85 ++ .../LRMOP/LRMOP6.m | 83 ++ .../Real-world MOPs/Sparse_KP.m | 2 +- .../ISCSO/ISCSO_2016.p | Bin 0 -> 4060 bytes .../ISCSO/ISCSO_2017.p | Bin 0 -> 4402 bytes .../ISCSO/ISCSO_2018.p | Bin 0 -> 5198 bytes .../ISCSO/ISCSO_2019.p | Bin 0 -> 4966 bytes .../ISCSO/ISCSO_2021.p | Bin 0 -> 5617 bytes .../ISCSO/ISCSO_2022.p | Bin 0 -> 5397 bytes .../ISCSO/SO_ISCSO_2016.m | 41 + .../ISCSO/SO_ISCSO_2017.m | 41 + .../ISCSO/SO_ISCSO_2018.m | 41 + .../ISCSO/SO_ISCSO_2019.m | 41 + .../ISCSO/SO_ISCSO_2021.m | 41 + .../ISCSO/SO_ISCSO_2022.m | 40 + PlatEMO/Problems/UserProblem.m | 54 +- PlatEMO/manual (Chinese).pdf | Bin 1653296 -> 1736861 bytes PlatEMO/manual.pdf | Bin 1616135 -> 1684127 bytes PlatEMO/platemo.m | 6 +- 110 files changed, 6293 insertions(+), 512 deletions(-) create mode 100644 PlatEMO/Algorithms/Blocks/BLOCK.p create mode 100644 PlatEMO/Algorithms/Blocks/BlockEA.m create mode 100644 PlatEMO/Algorithms/Blocks/Block_Crossover.m create mode 100644 PlatEMO/Algorithms/Blocks/Block_Crossover.p create mode 100644 PlatEMO/Algorithms/Blocks/Block_Exchange.m create mode 100644 PlatEMO/Algorithms/Blocks/Block_Exchange.p create mode 100644 PlatEMO/Algorithms/Blocks/Block_Kopt.m create mode 100644 PlatEMO/Algorithms/Blocks/Block_Kopt.p create mode 100644 PlatEMO/Algorithms/Blocks/Block_Mutation.m create mode 100644 PlatEMO/Algorithms/Blocks/Block_Mutation.p create mode 100644 PlatEMO/Algorithms/Blocks/Block_Population.m create mode 100644 PlatEMO/Algorithms/Blocks/Block_Population.p create mode 100644 PlatEMO/Algorithms/Blocks/Block_Selection.m create mode 100644 PlatEMO/Algorithms/Blocks/Block_Selection.p create mode 100644 PlatEMO/Algorithms/Blocks/Block_Tournament.m create mode 100644 PlatEMO/Algorithms/Blocks/Block_Tournament.p create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/ACMMEA.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/CalFitness.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/Clustering.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/CpuGroup.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/EnvironmentalSelection.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/EnvironmentalSelectionS.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/Operator.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/OperatorGAhalfSMM.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/SubPopRank.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/SubPopSimility.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/UpdateGV.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/LDS-AF/LDSAF.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/LDS-AF/RBFN_P.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/LDS-AF/RBFN_PP.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/LDS-AF/SolutionSelection.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/ArchUpdate.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/Construction.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/EnvironmentalSelection.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/Final.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/FitnessCal.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/Initialization.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/LRMOEA.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/LROperator.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/archives.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/BinaryCrossover.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/BinaryMutation.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/EnvironmentalSelection.m create mode 100644 PlatEMO/Algorithms/Multi-objective 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You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + methods + function main(Algorithm,Problem) + %% Parameter setting + [Blocks,Graph] = Algorithm.ParameterSet({},{}); + + %% Generate random population + isPop = arrayfun(@(s)isa(s,'Block_Population'),Blocks(:)'); + Blocks(isPop).Initialization(Problem.Initialization()); + + %% Optimization + while Algorithm.NotTerminated(Blocks(1).output) + activated = false(1,length(Blocks)); + while ~all(activated(isPop)) + for i = find(~activated) + if all(activated(logical(Graph(:,i)))|isPop(logical(Graph(:,i)))) + Blocks(i).Main(Problem,Blocks(logical(Graph(:,i))),Graph(:,i)); + activated(i) = true; + end + end + end + end + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Blocks/Block_Crossover.m b/PlatEMO/Algorithms/Blocks/Block_Crossover.m new file mode 100644 index 000000000..c3e2af0d4 --- /dev/null +++ b/PlatEMO/Algorithms/Blocks/Block_Crossover.m @@ -0,0 +1,13 @@ +% classdef Block_Crossover < BLOCK +% Unified crossover for real variables +% nParents --- 2 --- Number of parents generating one offspring +% nSets --- 5 --- Number of parameter sets + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- \ No newline at end of file diff --git a/PlatEMO/Algorithms/Blocks/Block_Crossover.p b/PlatEMO/Algorithms/Blocks/Block_Crossover.p new file mode 100644 index 0000000000000000000000000000000000000000..0604c1a5a2451400bc198c65c247805635cea72c GIT binary patch literal 759 zcmV|v zA!;XC4Wh}!sOqxYr2K}UkMKj6dU_ULf-*%ou_48lA?``OBL_5tExU1U02HQ4jKbE% z*xI2NsgB3$1CuVbNQ6dAM-@-SZUECj(hG=#2CX>~a4VmGPlr6$rwcbDHk3_Vhf&l( znX=gfONjL?x0fX|`@8RmhDa#dONGMOH(d8aau~hLI%2pI)$gkz>S3&-`P8cwQlkEr z`xpOyO2#~Do-}4mg+oehow*yG2gi*2mY09GrTqbLd<0e#kK=0oCjr!SB&Hm^4@>Y0CLj9t4v}+2_oOUV$Kv?;9;b<Y1fOs+7Ea5?ZDzHJ37~j){v!j^x_%8RB(Guag)|Ir2uS8nG$hJGV0a zkOOhWDp;o;~WK!l?l!`Be4%6w< zc6XCtsU^+da80l;%cZHJ=si(~^N1A!BlieY86T!<==C)f?^>#`KFsY_y(9RGn={&E z!s&|}^Uk!jrCgqW(qbQz__@SbDTz;mMExuXZ><+*wKVi>ZOKzd=0Ltf`5E5%W>6Ug z4U8f-^}K{+EoPL^$I=dHd6cKZ1gsy;QbV@V1)H4JgiwaEt{e{@E(imXXj(&EK;x1} zJ->^m2T}E7(0PH7X*5TbT2w{I2O6R{RKr|vL&h7Z2MA^$mFUo3^=Ek2t3Y19RtV8^ z>OY*Z<(a6ICXt)yqCr~0j7DAC?XjzemR~IGm0<>3@xK(>1+Y}Mcp><h7t`z}3R^gBQnt!b((AIJ~J36>-Nhfe^<=n9nc4i-*mH(ZsBsIKoDr phDPYBGO1>Dx3KyI%;`46C8{wCI8I>BBM>g{o$r{$$w>ay zNi`j2!ym0mQN|#kil6+ld)24OncjtU=w}}F<4Re zoo&I))2>c8L`L^CHBr|R{t;<@a&3{PqE@ZXYm{Bjs{?ahu+46!2a+WPLS4O#oz-oe z^Um7-+RNC0gJuu`8Z0G;qri8;-5@#A?z3!r&wZ;6(RsD^VkQ6rX0!N3Me(Q(ptfTG z%%6>WKylQd5vCJQs9zsQFpDh)(Ns6h70s89_AG!drV$ppxexZWuyVmB_Fn@OV9hpA zfDW-+Ui_ywT{?d~f60~y-eGMEZ!%~TMpECQF zJnDZ_3`m7`XU8CH@ZolBo-x;s3N)kWl-YRr;xhqeBh7E|uRdFjDLIdjs_7l~u_=<* A2mk;8 literal 0 HcmV?d00001 diff --git a/PlatEMO/Algorithms/Blocks/Block_Kopt.m b/PlatEMO/Algorithms/Blocks/Block_Kopt.m new file mode 100644 index 000000000..1dd6bc5e9 --- /dev/null +++ b/PlatEMO/Algorithms/Blocks/Block_Kopt.m @@ -0,0 +1,12 @@ +% classdef Block_Kopt < BLOCK +% k-opt +% k --- 4 --- Max number of k for k-opt + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- \ No newline at end of file diff --git a/PlatEMO/Algorithms/Blocks/Block_Kopt.p b/PlatEMO/Algorithms/Blocks/Block_Kopt.p new file mode 100644 index 0000000000000000000000000000000000000000..6215156a133272187989022dc12710f7eb4af7f5 GIT binary patch literal 520 zcmV+j0{8uPFflGLFm^C7E-)|v0RSBKtG}@T00CwI00HO#00QzH$qULknIisu&Dnwz zP_PuX!reGoBE&X@Tjbr3G>0-qf)_Y{i+gNM(_e}DVHN2<;yD}5fy;X)n3DoZ3#pfR z@Ca67AE?J=YN3-3+Au-n1erWPsic4!VPr)l zJ0{T!Zm31-B3_awu_t*$%u}4&ILoEhGJ=c7V4CM%N5A|niRdS)uz9&re<-1uM;~>B zSad1)UFut&^#|A?Gb66hAF^V?VhQUzgIyFK9gdrlXhWCkf>TaqY6gS!%`7L1o0|tK z0d6Ov{z<-=xQSt@yZ1$V-wbu9X^JrqGQ?|cg{mzFi%`rQi*9+R%4N=FI#7AXi_$SG zx~cLMhXIMSkErG!WM&&X3gU|G_&S>_sh2t3cyvd}QcnAstvNtBh^Mv`5dpP#5u7

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You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- \ No newline at end of file diff --git a/PlatEMO/Algorithms/Blocks/Block_Tournament.p b/PlatEMO/Algorithms/Blocks/Block_Tournament.p new file mode 100644 index 0000000000000000000000000000000000000000..fed60813d524697bf390bb31ee5da2bee4b62003 GIT binary patch literal 471 zcmV;|0Vw`lK_X&eJ*Wwby5$>i!! zzq02};~JbDxF&*mKoka zeFC_CbqHbk9uK8(qqZQFRht+5lse-qQZ>-2Sl4=UwBrH|{92HRpCzikScPu{&!1=19XE@PKf%npps3vsWJAKiG)yYoCChz zj0Cgy7XK!L45_};e$X`iLE=vj=385Zq`y-z2Z|u_+4c4Di^l6(Z&8oRJL=nqg-OF3 z@A&Ak=BtY6=7TPaL4$7(74^sTdH)Wk$~7S$ppqc~aJX~j+E@nYEy&Y`sQUqq1nGzb NA+MMK+G$`?@T5vx=ZydW literal 0 HcmV?d00001 diff --git a/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/ACMMEA.m b/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/ACMMEA.m new file mode 100644 index 000000000..d1d6bcd6d --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/ACMMEA.m @@ -0,0 +1,121 @@ +classdef ACMMEA < ALGORITHM +% +% Adaptive merging and coordinated offspring generation based multi-modal multi-objective evolutionary algorithm + +%------------------------------- Reference -------------------------------- +% X. Wang, T. Zheng, and Y. Jin, Adaptive merging and coordinated offspring +% generation in multi-population evolutionary multi-modal multi-objective +% optimization, Proceedings of the International Conference on Data-driven +% Optimization of Complex Systems, 2023. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Xiangyu Wang (email: xiangyu.wang@uni-bielefeld.de) + + methods + function main(Algorithm,Problem) + %% Population initialization + Dec = unifrnd(repmat(Problem.lower,Problem.N,1),repmat(Problem.upper,Problem.N,1)); + Mask = zeros(Problem.N,Problem.D); + GV = ones(1,Problem.D); + for i = 1 : Problem.N + Mask(i,TournamentSelection(2,ceil(rand*Problem.D),GV)) = 1; + GV(Mask(i,:)==1) = GV(Mask(i,:)==1)+1; + end + Population = Problem.Evaluation(Dec.*Mask); + [slst] = Clustering(Population.decs, 20, [Problem.lower,Problem.upper], Problem.D); + K=size(slst,2); + Masks = cell(1,K); + Decs = cell(1,K); + Populations = cell(1,K); + GV = cell(1,K); + FrontNo = cell(1,K); + CrowdDis = cell(1,K); + Fitness = cell(1,K); + for i = 1 : K + Populations{i} = Population(slst{i}); + Masks{i} = Mask(slst{i},:); + Decs{i} = Dec(slst{i},:); + [Populations{i},Decs{i},Masks{i},FrontNo{i},CrowdDis{i}] = EnvironmentalSelection(Populations{i},Decs{i},Masks{i},length(Populations{i})); + GV{i} = UpdateGV(zeros(1,Problem.D),Masks{i},FrontNo{i}); + end + StageIFlag = 1; + Timea = 0; + + %% Optimization + while Algorithm.NotTerminated(Population) + if Problem.FE < 0.3 * Problem.maxFE + [~,rank] = sort(SubPopRank(Populations)); + for i = 1 : K + GV{rank(i)} = UpdateGV(GV{rank(i)},Masks{rank(i)},FrontNo{rank(i)}); + Mating = TournamentSelection(2,2*length(Populations{rank(i)}),FrontNo{rank(i)},-CrowdDis{rank(i)}); + [OffDec,OffMask] = Operator(Problem,Decs{rank(i)}(Mating,:),Masks{rank(i)}(Mating,:),GV{rank(i)}, StageIFlag); + Offspring = Problem.Evaluation(OffDec.*OffMask); + Populations{rank(i)} = [Populations{rank(i)},Offspring]; + Decs{rank(i)} = [Decs{rank(i)};OffDec]; + Masks{rank(i)} = [Masks{rank(i)};OffMask]; + if i > 1 + for j = 1 : i-1 + [~,fs(rank(j))] = min(mean(Populations{rank(j)}.objs,2)); + end + R = zeros(1,Problem.D); + for j = 1 : i-1 + R = R + Masks{rank(j)}(fs(rank(j)),:); + end + R(R>0) = 1; + dis = sum(repmat(R,length(Populations{rank(i)}),1)&Masks{rank(i)},2); + [Populations{rank(i)},Decs{rank(i)},Masks{rank(i)},FrontNo{rank(i)},CrowdDis{rank(i)}] = EnvironmentalSelection(Populations{rank(i)},Decs{rank(i)},Masks{rank(i)},floor(Problem.N/K),dis); + else + [Populations{rank(i)},Decs{rank(i)},Masks{rank(i)},FrontNo{rank(i)},CrowdDis{rank(i)}] = EnvironmentalSelection(Populations{rank(i)},Decs{rank(i)},Masks{rank(i)},floor(Problem.N/K)); + end + end + if mod(ceil(Problem.FE/Problem.N),50)==0 + [Populations,Masks,Decs,GV,K]=SubPopSimility(Populations,Masks,Decs,GV); + FrontNo = cell(1,K); + CrowdDis = cell(1,K); + for i = 1 : K + [Populations{i},Decs{i},Masks{i},FrontNo{i},CrowdDis{i}] = EnvironmentalSelection(Populations{i},Decs{i},Masks{i},floor(Problem.N/K)); + end + end + else + if Timea == 0 + for i = 1 : K + [Populations{rank(i)},Decs{rank(i)},Masks{rank(i)},FrontNo{rank(i)},Fitness{rank(i)}] = EnvironmentalSelectionS(Populations{rank(i)},Decs{rank(i)},Masks{rank(i)},length(Populations{rank(i)})); + GV{rank(i)} = UpdateGV(zeros(1,Problem.D),Masks{rank(i)},FrontNo{rank(i)}); + end + end + Timea = Timea + 1; + StageIFlag = 0; + for i=1:K + GV{rank(i)} = UpdateGV(GV{rank(i)},Masks{rank(i)},FrontNo{rank(i)}); + Mating = TournamentSelection(2,2*length(Populations{rank(i)}),FrontNo{rank(i)},Fitness{rank(i)}); + [OffDec,OffMask] = Operator(Problem,Decs{rank(i)}(Mating,:),Masks{rank(i)}(Mating,:),GV{rank(i)}, StageIFlag); + Offspring = Problem.Evaluation(OffDec.*OffMask); + Populations{rank(i)} = [Populations{rank(i)},Offspring]; + Decs{rank(i)} = [Decs{rank(i)};OffDec]; + Masks{rank(i)} = [Masks{rank(i)};OffMask]; + [Populations{rank(i)},Decs{rank(i)},Masks{rank(i)},FrontNo{rank(i)},Fitness{rank(i)}] = EnvironmentalSelectionS(Populations{rank(i)},Decs{rank(i)},Masks{rank(i)},floor(Problem.N/K)); + + end + if mod(ceil(Problem.FE/Problem.N),50)==0 + [Populations,Masks,Decs,GV,K]=SubPopSimility(Populations,Masks,Decs,GV); + FrontNo = cell(1,K); + CrowdDis = cell(1,K); + for i = 1 : K + [Populations{i},Decs{i},Masks{i},FrontNo{i},CrowdDis{i}] = EnvironmentalSelection(Populations{i},Decs{i},Masks{i},floor(Problem.N/K)); + end + Timea = 0; + [~,rank] = sort(SubPopRank(Populations)); + end + end + Population = [Populations{:}]; + end + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/CalFitness.m b/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/CalFitness.m new file mode 100644 index 000000000..ab4aaa502 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/CalFitness.m @@ -0,0 +1,47 @@ +function Fitness = CalFitness(PopObj) +% Calculate the fitness of each solution + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Xiangyu Wang (email: xiangyu.wang@uni-bielefeld.de) + + N = size(PopObj,1); + + %% Detect the dominance relation between each two solutions + Dominate = false(N); + for i = 1 : N-1 + for j = i+1 : N + k = any(PopObj(i,:)PopObj(j,:)); + if k == 1 + Dominate(i,j) = true; + elseif k == -1 + Dominate(j,i) = true; + end + end + end + + %% Calculate S(i) + S = sum(Dominate,2); + + %% Calculate R(i) + R = zeros(1,N); + for i = 1 : N + R(i) = sum(S(Dominate(:,i))); + end + + %% Calculate D(i) + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Distance = sort(Distance,2); + D = 1./(Distance(:,floor(sqrt(N)))+2); + + %% Calculate the fitnesses + Fitness = R + D'; +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/Clustering.m b/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/Clustering.m new file mode 100644 index 000000000..40616cb9e --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/Clustering.m @@ -0,0 +1,70 @@ +function slst = Clustering(X, n, bound, dim) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Xiangyu Wang (email: xiangyu.wang@uni-bielefeld.de) + + a = size(X,1); + p = X; + p(:,dim+1) = [1:a]; + for i = 1 : a + G{i} = i; + end + M = []; + for j = 1 : a + M(j,:) = ((sum((repmat(X(j,:),size(X,1),1) - X).^2,2)).^0.5)';%M(i, j) reprents the distance of i-th cluster to the j-th cluster + end + found = 1; + while found == 1 + found = 0; + min_dist = (dim*((bound(2) - bound(1)).^2)).^0.5; + for i = 1:size(G,2)-1 + for j = i + 1 :size(G,2) + if size(G{i},2) + size(G{j},2) < n+1 + if min_dist >= M(i, j) + min_dist = M(i, j); + r = i; + s = j; + found = 1; + end + end + end + + end + if ~isempty(r) && ~isempty(s) + G{r} = [G{r}, G{s}]; % merge clusters r and s; + for k = 1 : size(G{s},2) + p(p(:,dim+1)==G{s}(k),:) = []; + end + G(s) = []; % delete cluster s; + M(:,s) = []; % update the M + M(s,:) = []; % update the M + h = p(:,1:dim); + for k = 1 : size(G{r},2) + D(k,:) = ((sum((repmat(X(G{r}(k),:),size(h,1),1) - h).^2,2)).^0.5)'; + end + D = min(D,[],1); + M(:,r) = D';M(r,:) = D; + D = []; + s = []; + r = []; + c = 0; + for i = 1 : size(G,2) + if size(G{i},2) == 1 + c = c + 1; + end + end + if c == 0 + found = 0; + end + end + end + slst = G; +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/CpuGroup.m b/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/CpuGroup.m new file mode 100644 index 000000000..68ce841cf --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/CpuGroup.m @@ -0,0 +1,34 @@ +function [Index,MAX] = CpuGroup(numberOfGroups,xPrime,numberOfVariables) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Xiangyu Wang (email: xiangyu.wang@uni-bielefeld.de) + + varsPerGroup = floor(numberOfVariables/numberOfGroups); + if varsPerGroup == 1 + Index = linspace(1,numberOfVariables,numberOfVariables); + MAX = numberOfVariables; + else + B = ones(1,varsPerGroup*numberOfGroups); + remain = ones(1,(numberOfVariables-varsPerGroup*numberOfGroups))*(numberOfGroups+1); + R = reshape(B,varsPerGroup,numberOfGroups); + k = linspace(1,numberOfGroups,numberOfGroups); + index = R.*repmat(k,varsPerGroup,1); + index = reshape(index,1,varsPerGroup*numberOfGroups); + INDEX = [index remain]; + [~,I] = sort(xPrime); + Index(I) = INDEX; + if(mod(numberOfVariables,numberOfGroups)==0) + MAX = numberOfGroups; + else + MAX = numberOfGroups + 1; + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/EnvironmentalSelection.m b/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/EnvironmentalSelection.m new file mode 100644 index 000000000..9210126b0 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/EnvironmentalSelection.m @@ -0,0 +1,45 @@ +function [Population,Dec,Mask,FrontNo,CrowdDis] = EnvironmentalSelection(Population,Dec,Mask,N,dis) +% The environmental selection of MP-MMEA + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + if nargin > 4 + PopObj = [Population.objs,dis]; + else + PopObj = Population.objs; + end + + %% Delete duplicated solutions + [~,uni] = unique(PopObj,'rows'); + PopObj = PopObj(uni,:); + Population = Population(uni); + Dec = Dec(uni,:); + Mask = Mask(uni,:); + N = min(N,length(Population)); + + %% Non-dominated sorting + [FrontNo,MaxFNo] = NDSort(PopObj,Population.cons,N); + Next = FrontNo < MaxFNo; + + %% Calculate the crowding distance of each solution + CrowdDis = CrowdingDistance(PopObj,FrontNo); + + %% Select the solutions in the last front based on their crowding distances + Last = find(FrontNo==MaxFNo); + [~,Rank] = sort(CrowdDis(Last),'descend'); + Next(Last(Rank(1:N-sum(Next)))) = true; + + %% Population for next generation + Population = Population(Next); + FrontNo = FrontNo(Next); + CrowdDis = CrowdDis(Next); + Dec = Dec(Next,:); + Mask = Mask(Next,:); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/EnvironmentalSelectionS.m b/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/EnvironmentalSelectionS.m new file mode 100644 index 000000000..ad746f877 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/EnvironmentalSelectionS.m @@ -0,0 +1,66 @@ +function [Population,Dec,Mask,FrontNo,Fitness] = EnvironmentalSelectionS(Population,Dec,Mask,N,dis) +% The environmental selection of AC-MMEA + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Xiangyu Wang (email: xiangyu.wang@uni-bielefeld.de) + + if nargin > 4 + PopObj = [Population.objs,dis]; + else + PopObj = Population.objs; + end + + %% Delete duplicated solutions + [~,uni] = unique(PopObj,'rows'); + PopObj = PopObj(uni,:); + Population = Population(uni); + Dec = Dec(uni,:); + Mask = Mask(uni,:); + N = min(N,length(Population)); + + %% Calculate the fitness of each solution + Fitness = CalFitness(PopObj); + + %% Non-dominated sorting + [FrontNo,~] = NDSort(PopObj,Population.cons,N); + + %% Environmental selection + Next = Fitness < 1; + if sum(Next) < N + [~,Rank] = sort(Fitness); + Next(Rank(1:N)) = true; + elseif sum(Next) > N + Del = Truncation(Population(Next).objs,sum(Next)-N); + Temp = find(Next); + Next(Temp(Del)) = false; + end + + %% Population for next generation + Population = Population(Next); + FrontNo = FrontNo(Next); + Fitness = Fitness(Next); + Dec = Dec(Next,:); + Mask = Mask(Next,:); +end +function Del = Truncation(PopObj,K) +% Select part of the solutions by truncation + + %% Truncation + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Del = false(1,size(PopObj,1)); + while sum(Del) < K + Remain = find(~Del); + Temp = sort(Distance(Remain,Remain),2); + [~,Rank] = sortrows(Temp); + Del(Remain(Rank(1))) = true; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/Operator.m b/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/Operator.m new file mode 100644 index 000000000..c96da0dc7 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/Operator.m @@ -0,0 +1,63 @@ +function [OffDec,OffMask] = Operator(Problem,ParentDec,ParentMask,Score, StageIFlag) +% Operator of AC-MMEA + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Xiangyu Wang (email: xiangyu.wang@uni-bielefeld.de) + + %% Parameter setting + N = size(ParentDec,1); + Parent1Mask = ParentMask(1:N/2,:); + Parent2Mask = ParentMask(N/2+1:end,:); + + %% Crossover for mask + OffMask = Parent1Mask; + for i = 1 : N/2 + index1 = find(Parent1Mask(i,:)&~Parent2Mask(i,:)); + index2 = find(~Parent1Mask(i,:)&Parent2Mask(i,:)); + p1 = 1./(1+exp(-Score(index1))); + p2 = 1./(1+exp(-Score(index2))); + idx1 = index1(p1rand(size(p2))); + OffMask(i,idx1) = 0; + OffMask(i,idx2) = 1; + end + + %% Mutation for mask + for i = 1 : N/2 + if rand < 0.5 + index = find(OffMask(i,:)); + index = index(TS(Score(index))); + OffMask(i,index) = 0; + else + index = find(~OffMask(i,:)); + index = index(TS(-Score(index))); + OffMask(i,index) = 1; + end + end + + %% select the suitable index to give highly mutation + % using the group method by SLMEA + Ratio = sum(OffMask,1)./N; + [indexM,MAX] = CpuGroup(5,Ratio,size(ParentDec,2)); + + %% Crossover and mutation for dec + OffDec = OperatorGAhalfSMM(Problem, ParentDec, indexM, MAX,StageIFlag, {1,20,1,0.5}); +end + +function index = TS(Fitness) +% Binary tournament selection + + if isempty(Fitness) + index = []; + else + index = TournamentSelection(2,1,Fitness); + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/OperatorGAhalfSMM.m b/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/OperatorGAhalfSMM.m new file mode 100644 index 000000000..c27c93676 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/OperatorGAhalfSMM.m @@ -0,0 +1,202 @@ +function Offspring = OperatorGAhalfSMM(Problem,Parent, indexM, MAX, Flag, Parameter) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Xiangyu Wang (email: xiangyu.wang@uni-bielefeld.de) + + if nargin > 4 + [proC,disC,proM,disM] = deal(Parameter{:}); + else + [proC,disC,proM,disM] = deal(1,20,1,20); + end + if isa(Parent(1),'SOLUTION') + evaluated = true; + Parent = Parent.decs; + else + evaluated = false; + end + Parent1 = Parent(1:floor(end/2),:); + Parent2 = Parent(floor(end/2)+1:floor(end/2)*2,:); + Offspring = zeros(size(Parent1,1),size(Parent1,2)); + Type = arrayfun(@(i)find(Problem.encoding==i),1:5,'UniformOutput',false); + if ~isempty([Type{1:2}]) % Real and integer variables + Offspring(:,[Type{1:2}]) = GAreal(Parent1(:,[Type{1:2}]),Parent2(:,[Type{1:2}]),Problem.lower([Type{1:2}]),Problem.upper([Type{1:2}]),proC,disC,proM,disM, indexM, MAX, Flag); + end + if ~isempty(Type{3}) % Label variables + Offspring(:,Type{3}) = GAlabel(Parent1(:,Type{3}),Parent2(:,Type{3}),proC,proM); + end + if ~isempty(Type{4}) % Binary variables + Offspring(:,Type{4}) = GAbinary(Parent1(:,Type{4}),Parent2(:,Type{4}),proC,proM); + end + if ~isempty(Type{5}) % Permutation variables + Offspring(:,Type{5}) = GApermutation(Parent1(:,Type{5}),Parent2(:,Type{5}),proC); + end + if evaluated + Offspring = Problem.Evaluation(Offspring); + end +end + +function Offspring = GAreal(Parent1,Parent2,lower,upper,proC,disC,proM,disM, indexM, MAX, Flag) +% Genetic operators for real and integer variables + + %% Simulated binary crossover + [N,D] = size(Parent1); + beta = zeros(N,D); + mu = rand(N,D); + beta(mu<=0.5) = (2*mu(mu<=0.5)).^(1/(disC+1)); + beta(mu>0.5) = (2-2*mu(mu>0.5)).^(-1/(disC+1)); + beta = beta.*(-1).^randi([0,1],N,D); + beta(rand(N,D)<0.5) = 1; + beta(repmat(rand(N,1)>proC,1,D)) = 1; + Offspring = (Parent1+Parent2)/2+beta.*(Parent1-Parent2)/2; + + %% Polynomial mutation + Lower = repmat(lower,N,1); + Upper = repmat(upper,N,1); + if Flag + % highest mutation + Site = rand(N,D) < 4 * proM/D; + mu = rand(N,D); + indx_mu = indexM == MAX ; + temp = Site & mu<=0.5 & indx_mu; + Offspring = min(max(Offspring,Lower),Upper); + Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*((2.*mu(temp)+(1-2.*mu(temp)).*... + (1-(Offspring(temp)-Lower(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))-1); + temp = Site & mu>0.5 & indx_mu; + Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*(1-(2.*(1-mu(temp))+2.*(mu(temp)-0.5).*... + (1-(Upper(temp)-Offspring(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))); + + % highest mutation + Site = rand(N,D) < 4 * proM/D; + mu = rand(N,D); + indx_mu = indexM == MAX-2 | indexM == MAX-1; + temp = Site & mu<=0.5 & indx_mu; + Offspring = min(max(Offspring,Lower),Upper); + Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*((2.*mu(temp)+(1-2.*mu(temp)).*... + (1-(Offspring(temp)-Lower(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))-1); + temp = Site & mu>0.5 & indx_mu; + Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*(1-(2.*(1-mu(temp))+2.*(mu(temp)-0.5).*... + (1-(Upper(temp)-Offspring(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))); + + % highest mutation + Site = rand(N,D) < 4 * proM/D ; + mu = rand(N,D); + indx_mu = indexM == MAX-3 | indexM == MAX-4; + temp = Site & mu<=0.5 & indx_mu; + Offspring = min(max(Offspring,Lower),Upper); + Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*((2.*mu(temp)+(1-2.*mu(temp)).*... + (1-(Offspring(temp)-Lower(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))-1); + temp = Site & mu>0.5 & indx_mu; + Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*(1-(2.*(1-mu(temp))+2.*(mu(temp)-0.5).*... + (1-(Upper(temp)-Offspring(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))); + else + % highest mutation + Site = rand(N,D) < 4 * proM/D; + mu = rand(N,D); + indx_mu = indexM == MAX ; + temp = Site & mu<=0.5 & indx_mu; + Offspring = min(max(Offspring,Lower),Upper); + Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*((2.*mu(temp)+(1-2.*mu(temp)).*... + (1-(Offspring(temp)-Lower(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))-1); + temp = Site & mu>0.5 & indx_mu; + Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*(1-(2.*(1-mu(temp))+2.*(mu(temp)-0.5).*... + (1-(Upper(temp)-Offspring(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))); + + % high mutation + Site = rand(N,D) < 2 * proM/D; + mu = rand(N,D); + indx_mu = indexM == MAX-2 | indexM == MAX-1; + temp = Site & mu<=0.5 & indx_mu; + Offspring = min(max(Offspring,Lower),Upper); + Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*((2.*mu(temp)+(1-2.*mu(temp)).*... + (1-(Offspring(temp)-Lower(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))-1); + temp = Site & mu>0.5 & indx_mu; + Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*(1-(2.*(1-mu(temp))+2.*(mu(temp)-0.5).*... + (1-(Upper(temp)-Offspring(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))); + + % low mutation + Site = rand(N,D) < proM/D/2; + mu = rand(N,D); + indx_mu = indexM == MAX-3 | indexM == MAX-4; + temp = Site & mu<=0.5 & indx_mu; + Offspring = min(max(Offspring,Lower),Upper); + Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*((2.*mu(temp)+(1-2.*mu(temp)).*... + (1-(Offspring(temp)-Lower(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))-1); + temp = Site & mu>0.5 & indx_mu; + Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*(1-(2.*(1-mu(temp))+2.*(mu(temp)-0.5).*... + (1-(Upper(temp)-Offspring(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))); + end +end + +function Offspring = GAlabel(Parent1,Parent2,proC,proM) +% Genetic operators for label variables + + %% Uniform crossover + [N,D] = size(Parent1); + k = rand(N,D) < 0.5; + k(repmat(rand(N,1)>proC,1,D)) = false; + Offspring = Parent1; + Offspring(k) = Parent2(k); + + %% Bitwise mutation + Site = rand(N,D) < proM/D; + Rand = randi(D,N,D); + Offspring(Site) = Rand(Site); + + %% Repair + for i = 1 : N + Off = zeros(1,D); + while ~all(Off) + x = find(~Off,1); + Off(Offspring(i,:)==Offspring(i,x)) = max(Off) + 1; + end + Offspring(i,:) = Off; + end +end + +function Offspring = GAbinary(Parent1,Parent2,proC,proM) +% Genetic operators for binary variables + + %% Uniform crossover + [N,D] = size(Parent1); + k = rand(N,D) < 0.5; + k(repmat(rand(N,1)>proC,1,D)) = false; + Offspring = Parent1; + Offspring(k) = Parent2(k); + + %% Bit-flip mutation + Site = rand(N,D) < proM/D; + Offspring(Site) = ~Offspring(Site); +end + +function Offspring = GApermutation(Parent1,Parent2,proC) +% Genetic operators for permutation variables + + %% Order crossover + [N,D] = size(Parent1); + Offspring = Parent1; + k = randi(D,1,N); + for i = 1 : N + if rand < proC + Offspring(i,k(i)+1:end) = setdiff(Parent2(i,:),Parent1(i,1:k(i)),'stable'); + end + end + + %% Slight mutation + k = randi(D,1,N); + s = randi(D,1,N); + for i = 1 : N + if s(i) < k(i) + Offspring(i,:) = Offspring(i,[1:s(i)-1,k(i),s(i):k(i)-1,k(i)+1:end]); + elseif s(i) > k(i) + Offspring(i,:) = Offspring(i,[1:k(i)-1,k(i)+1:s(i)-1,k(i),s(i):end]); + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/SubPopRank.m b/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/SubPopRank.m new file mode 100644 index 000000000..e55498f0d --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/SubPopRank.m @@ -0,0 +1,30 @@ +function [FNmean,FNbest] = SubPopRank(Populations) +% Calculate the mean and best front number of each subpopulation + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Xiangyu Wang (email: xiangyu.wang@uni-bielefeld.de) + + Population = [Populations{:}]; + K = length(Populations); + Flag = []; + j = 1; + for i = 1 : K + Flag(j:j+length(Populations{i})-1) = i; + j = j + length(Populations{i}); + end + FrontNoAll = NDSort(Population.objs,inf); + FNmean = zeros(1,K); + FNbest = zeros(1,K); + for i = 1 : K + FNmean(i) = mean(FrontNoAll(Flag==i)); + FNbest(i) = min(FrontNoAll(Flag==i)); + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/SubPopSimility.m b/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/SubPopSimility.m new file mode 100644 index 000000000..ef75d2cb7 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/SubPopSimility.m @@ -0,0 +1,85 @@ +function [Populations2,Masks2,Decs2,GV2,numK] = SubPopSimility(Populations,Masks,Decs,GV) +% Calculate the similarity between subpopulations + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Xiangyu Wang (email: xiangyu.wang@uni-bielefeld.de) + + K = length(Populations); + fs = zeros(1,K); + for m = 1 : K + [~,fs(m)] = min(mean(Populations{m}.objs,2)); + end + ss = 0; + flagmerge=zeros(K,1); + for i = 1 : K-1 + if flagmerge(i,:)==0||flagmerge(i,:)==i + for j = i+1 : K + if flagmerge(j,:)==0 + ss = simility(Masks{i}(fs(i),:),Masks{j}(fs(j),:)); + if ss>0.7 + flagmerge(i,:)=i; + flagmerge(j,:)=i; + end + else + continue; + end + end + else + continue; + end + end + a = find(flagmerge==0); + b = max(flagmerge); + numK = size(a,1) + b; + for k = unique(flagmerge)' + temP2 = []; + temD2 = []; + temM2 = []; + temG2 = []; + current = find(flagmerge==k); + if k ~= 0 + for h = 1 : size(current,1) + temP = Populations{current(h)}; + temP2 = [temP2,temP]; + temD = Decs{current(h)}; + temD2 = [temD2;temD]; + temM = Masks{current(h)}; + temM2 = [temM2;temM]; + temG = GV{current(h)}; + temG2 = [temG2;temG]; + end + Populations2{k+size(a,1)} = temP2; + Decs2{k+size(a,1)} = temD2; + Masks2{k+size(a,1)} = temM2; + GV2{k+size(a,1)} = temG2; + else + for h = 1 : size(current,1) + temP = Populations{current(h)}; + temD = Decs{current(h)}; + temM = Masks{current(h)}; + temG = GV{current(h)}; + Populations2{h} = temP; + Decs2{h} = temD; + Masks2{h}= temM; + GV2{h} = temG; + end + end + end + Populations2(cellfun(@isempty,Populations2)) = []; + Decs2(cellfun(@isempty,Decs2)) = []; + Masks2(cellfun(@isempty,Masks2)) = []; + GV2(cellfun(@isempty,GV2)) = []; + numK = size(Populations2,2); +end + +function s = simility(subPop1,subPop2) + s = sum(subPop1&subPop2)/min(sum(subPop1),sum(subPop2)); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/UpdateGV.m b/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/UpdateGV.m new file mode 100644 index 000000000..07f05ec35 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/AC-MMEA/UpdateGV.m @@ -0,0 +1,32 @@ +function gv = UpdateGV(gv,Mask,FrontNo) +% Update the guiding vectors + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Xiangyu Wang (email: xiangyu.wang@uni-bielefeld.de) + + Mask = Mask(FrontNo==1,:); + k = ceil(0.1*size(Mask,1)); + v = zeros(1,size(Mask,2)); + for i = 1 : size(Mask,1) + rand_Mask = Mask(i,:); + dis = pdist2(rand_Mask,Mask,'hamming'); + [~,index] = sort(dis); + knearest_Mask = Mask(index(1:k),:); + rand_Mask(rand_Mask==1) = 0.5 ; + knearest_Mask(knearest_Mask==1) = 0.5 ; + v = v + sum(repmat(rand_Mask,k,1)+knearest_Mask,1)/(size(Mask,1)*k); + end + if all(gv==0) + gv = v; + else + gv = 0.9*gv + 0.1*v; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMMO/CMMO.m b/PlatEMO/Algorithms/Multi-objective optimization/CMMO/CMMO.m index cf7251e6b..4a0972501 100644 --- a/PlatEMO/Algorithms/Multi-objective optimization/CMMO/CMMO.m +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMMO/CMMO.m @@ -35,7 +35,7 @@ function main(Algorithm,Problem) epsilon_k = epsilon_0; %% calculate fitness of populations - [Fitness1,D_Dec,D_Pop] = CalFitness(Population1.objs,Population1.decs); + [D_Dec,D_Pop,Fitness1] = CalFitness(Population1.objs,Population1.decs); [Fitness2,D] = CalFitnessDecEpsilon(Population2.objs,Population2.decs,epsilon_k); %% Optimization diff --git a/PlatEMO/Algorithms/Multi-objective optimization/LDS-AF/LDSAF.m b/PlatEMO/Algorithms/Multi-objective optimization/LDS-AF/LDSAF.m new file mode 100644 index 000000000..1a4bf3ba2 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/LDS-AF/LDSAF.m @@ -0,0 +1,98 @@ +classdef LDSAF < ALGORITHM +% % +% Low-dimensional surrogate aggregation function + +%------------------------------- Reference -------------------------------- +% H. Gu, H. Wang, C. He, B. Yuan, and Y. Jin, Large-scale multiobjective +% evolutionary algorithm guided by low-dimensional surrogates of +% scalarization functions, Evolutionary Computation, 2024. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Haoran Gu + + methods + function main(Algorithm, Problem) + %% Parameter setting + [delta,N_s] = Algorithm.ParameterSet(0.9, 20); + + %% Generate the weight vectors + [W, Problem.N] = UniformPoint(Problem.N, Problem.M); + + %% Detect the neighbours of each solution + T = ceil(Problem.N / 10); + B = pdist2(W, W); + [~, B] = sort(B, 2); + B = B(:, 1 : T); + + %% Initialize population + PopDec = UniformPoint(Problem.N, Problem.D, 'Latin'); + Population = Problem.Evaluation(repmat(Problem.upper - Problem.lower, Problem.N, 1) .* PopDec + repmat(Problem.lower, Problem.N, 1)); + A = Population; + Z = min(Population.objs, [], 1); + + %% Define RBFN + RBFN_list1 = RBFN_PP(Problem); + RBFN_list2 = RBFN_P(Problem); + P_center = B; + + %% Optimization + while Algorithm.NotTerminated(A) + % For each sub-problem + for i = 1 : Problem.N + %% Model-construction + if Problem.FE >= 350 + pdb = pdist2(W, W); + pds2 = pdb(i,B(i,:)); + farv = find(pds2>=(max(pds2)-0.00001)); + a = randperm(length(farv)); + + center = P_center(i,farv(a(1))); + + RBFN_list2(i) = RBFN_list2(i).ModelConstruction(A, B(i, :), W, Z,center); + model = RBFN_list2(i); + else + RBFN_list1(i) = RBFN_list1(i).ModelConstruction(A, B(i, :), W, Z); + model = RBFN_list1(i); + end + + %% Choose the parents + if rand < delta + P = B(i, randperm(end)); + else + P = randperm(Problem.N); + end + + %% Solution-generation + y_i = SolutionSelection(Problem, Population, P, model, N_s, i); + + %% Evaluate offspring + y_i = Problem.Evaluation(y_i); + + %% Update the reference point + Z = min(Z, y_i.obj); + if Problem.FE <= 2*Problem.N + nr = 2; + else + nr = 4; + end + + %% Update population and archive + g_old = max(abs(Population(P).objs - repmat(Z, length(P), 1)) .* W(P, :), [], 2); + g_new = max(repmat(abs(y_i.obj - Z), length(P), 1) .* W(P, :), [], 2); + Population(P(find(g_old >= g_new, nr))) = y_i; + A = [A, y_i]; + + %% Check termination criteria + Algorithm.NotTerminated(A); + end + end + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/LDS-AF/RBFN_P.m b/PlatEMO/Algorithms/Multi-objective optimization/LDS-AF/RBFN_P.m new file mode 100644 index 000000000..d2b32e5fb --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/LDS-AF/RBFN_P.m @@ -0,0 +1,85 @@ +classdef RBFN_P + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Haoran Gu + + properties + Problem; + index; + x; + trainsample; + vec; + d; + lam; + end + methods + %% Constructor + function obj = RBFN_P(Problem) + if nargin == 1 + obj(1, Problem.N) = RBFN_P; + for i = 1 : length(obj) + obj(i).index = i; + obj(i).Problem = Problem; + obj(i).x = []; + obj(i).trainsample; + obj(i).vec; + obj(i).d; + obj(i).lam; + end + end + end + %% Model-construction + function obj = ModelConstruction(obj, A, B_i, W, Z,center) + indices = 1 : length(A); + for i = 1 : length(A) + obj.x(i, :) = A(i).dec; + end + g_data1 = max(abs(A(indices).objs - repmat(Z, length(indices), 1)) .* W(center, :), [], 2); + uniformed_xdata = zeros(length(A), obj.Problem.D); + for i = 1 : length(A) + uniformed_xdata(i, :) = obj.UniformInput(obj.x(i, :)); + end + covx = cov(uniformed_xdata); + [vec_pop,~,~] = pcacov(covx); + uniformed_xdata = uniformed_xdata* vec_pop; + obj.vec = vec_pop; + + obj.d = obj.Problem.D/2; + obj.trainsample = uniformed_xdata(:,1:obj.d); + pair = pdist2(obj.trainsample, obj.trainsample); + D_max = max(max(pair, [], 2)); + spread = D_max * (obj.Problem.D * obj.Problem.N) ^ (-1 / obj.Problem.D); + RBFModel = newrbe(transpose(obj.trainsample), transpose(g_data1), spread); + obj.lam = RBFModel; + end + function predicted_value = PredictClass(obj, x) + uniformed_xdata = zeros(size(obj.x,1), obj.Problem.D); + uniformed_x = zeros(size(x,1), obj.Problem.D); + for i = 1 : size(obj.x,1) + uniformed_xdata(i, :) = obj.UniformInput(obj.x(i, :)); + end + for j = 1 : size(x,1) + uniformed_x(j,:) = obj.UniformInput(x(j,:)); + end + uniformed_x = uniformed_x * obj.vec; + test_x = uniformed_x(:,1:obj.d); + predicted_value = transpose(sim(obj.lam, transpose(test_x))); + end + function uniformed_x = UniformInput(obj, x) + uniformed_x = ones(1, obj.Problem.D); + for i = 1 : obj.Problem.D + x_min = obj.Problem.lower(i); + x_max = obj.Problem.upper(i); + uniformed_x(i) = (x(i) - x_min) / (x_max - x_min); + end + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/LDS-AF/RBFN_PP.m b/PlatEMO/Algorithms/Multi-objective optimization/LDS-AF/RBFN_PP.m new file mode 100644 index 000000000..d6d54bfa8 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/LDS-AF/RBFN_PP.m @@ -0,0 +1,84 @@ +classdef RBFN_PP + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Haoran Gu + + properties + Problem; + index; + x; + trainsample; + vec; + d; + lam; + end + methods + %% Constructor + function obj = RBFN_PP(Problem) + if nargin == 1 + obj(1, Problem.N) = RBFN_PP; + for i = 1 : length(obj) + obj(i).index = i; + obj(i).Problem = Problem; + obj(i).x = []; + obj(i).trainsample; + obj(i).vec; + obj(i).d; + obj(i).lam; + end + end + end + function obj = ModelConstruction(obj, A, B_i, W, Z) + indices = 1 : length(A); + for i = 1 : length(A) + obj.x(i, :) = A(i).dec; + end + g_data1 = max(abs(A(indices).objs - repmat(Z, length(indices), 1)) .* W(B_i(1), :), [], 2); + uniformed_xdata = zeros(length(A), obj.Problem.D); + for i = 1 : length(A) + uniformed_xdata(i, :) = obj.UniformInput(obj.x(i, :)); + end + covx = cov(uniformed_xdata); + [vec_pop,~,~] = pcacov(covx); + uniformed_xdata = uniformed_xdata* vec_pop; + obj.vec = vec_pop; + + obj.d = obj.Problem.D/2; + obj.trainsample = uniformed_xdata(:,1:obj.d); + pair = pdist2(obj.trainsample, obj.trainsample); + D_max = max(max(pair, [], 2)); + spread = D_max * (obj.Problem.D * obj.Problem.N) ^ (-1 / obj.Problem.D); + RBFModel = newrbe(transpose( obj.trainsample), transpose(g_data1), spread); + obj.lam = RBFModel; + end + function predicted_value = PredictClass(obj, x) + uniformed_xdata = zeros(size(obj.x,1), obj.Problem.D); + uniformed_x = zeros(size(x,1), obj.Problem.D); + for i = 1 : size(obj.x,1) + uniformed_xdata(i, :) = obj.UniformInput(obj.x(i, :)); + end + for j = 1 : size(x,1) + uniformed_x(j,:) = obj.UniformInput(x(j,:)); + end + uniformed_x = uniformed_x * obj.vec; + test_x = uniformed_x(:,1:obj.d); + predicted_value = transpose(sim(obj.lam, transpose(test_x))); + end + function uniformed_x = UniformInput(obj, x) + uniformed_x = ones(1, obj.Problem.D); + for i = 1 : obj.Problem.D + x_min = obj.Problem.lower(i); + x_max = obj.Problem.upper(i); + uniformed_x(i) = (x(i) - x_min) / (x_max - x_min); + end + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/LDS-AF/SolutionSelection.m b/PlatEMO/Algorithms/Multi-objective optimization/LDS-AF/SolutionSelection.m new file mode 100644 index 000000000..959c80dd7 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/LDS-AF/SolutionSelection.m @@ -0,0 +1,23 @@ +function y_i = SolutionSelection(Problem, Population, P, model, N_s, i) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Haoran Gu + + Prnd = nchoosek(P,2); + Prnd = [Prnd;Prnd]; + Prnd(N_s/2+1:N_s,[1,2]) = Prnd(N_s/2+1:N_s,[2,1]); + for r = 1 : N_s + candidate(r,:) = OperatorDE(Problem, Population(i).dec, Population(Prnd(r,1)).dec, Population(Prnd(r,2)).dec); + end + c = model.PredictClass(candidate); + [~,pos] = min(c); + y_i = candidate(pos,:); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/LMEA/VariableClustering.m b/PlatEMO/Algorithms/Multi-objective optimization/LMEA/VariableClustering.m index f437c15e4..5003aeec7 100644 --- a/PlatEMO/Algorithms/Multi-objective optimization/LMEA/VariableClustering.m +++ b/PlatEMO/Algorithms/Multi-objective optimization/LMEA/VariableClustering.m @@ -45,8 +45,8 @@ RMSE(i,j) = sqrt(sum(error.^2)); % Calculate the angle between the line and the hyperplane normal = ones(1,size(Vector,2)); - sine = abs(sum(Vector.*normal,2))./norm(Vector)./norm(normal); - Angle(i,j) = real(asin(sine)/pi*180); + cosine = abs(sum(Vector.*normal,2))./norm(Vector)./norm(normal); + Angle(i,j) = real(acos(cosine)/pi*180); end end @@ -54,7 +54,7 @@ VariableKind = (mean(RMSE,2)<1e-2)'; result = kmeans(Angle,2)'; if any(result(VariableKind)==1) && any(result(VariableKind)==2) - if mean(mean(Angle(result==1&VariableKind,:))) > mean(mean(Angle(result==2&VariableKind,:))) + if mean(mean(Angle(result==1&VariableKind,:))) < mean(mean(Angle(result==2&VariableKind,:))) VariableKind = VariableKind & result==1; else VariableKind = VariableKind & result==2; diff --git a/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/ArchUpdate.m b/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/ArchUpdate.m new file mode 100644 index 000000000..0ed4528ba --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/ArchUpdate.m @@ -0,0 +1,59 @@ +function [Arch] = ArchUpdate(Problem,arch,TemArch,thea,eta) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + TDec = arch.decs; + Mask = arch.masks; + % Perturb Solutions + PopDec = TDec.*Mask; + Population = Problem.Perturb(PopDec,1); + + Pop = Population.objs; + arch = Memory(arch,Pop); + Mr = arch.mrs; + arch = arch(Mr<=thea); + if isempty(arch) + Arch = TemArch; + else + Tdec = (TemArch.decs).*(TemArch.masks); + Decs = (arch.decs).*(arch.masks); + L = size(Tdec,1); + n = size(Decs,1); + Tobj = TemArch.objs; + a = ones(L,1); + for i = 1 : L + for j = 1 : n + if all(Tdec(i,:)==Decs(j,:)) + a(i,1) = 0; + arch(j) = Memory(arch(j),Tobj(i,:)); + break; + end + end + end + t = TemArch(a==1); + if ~isempty(t) + maf = max(t.objs); + Tobjs = arch.objs; + inx = (sum(Tobjs,2)<(eta.*sum(maf))); + arch = arch(inx); + end + Arch = [arch,t]; + end +end + +function value = Memory(arch,pop) + for k = 1 : size(arch,2) + arch(k).mobj = [arch(k).mobj;pop(k,:)]; + arch(k).Gn = arch(k).Gn + 1; + ob = arch(k).mobj; + arch(k).mr = mean(mean(abs(ob-min(ob)))./mean(ob)); + end + value = arch; +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/Construction.m b/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/Construction.m new file mode 100644 index 000000000..80fc38b9c --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/Construction.m @@ -0,0 +1,66 @@ +function [rf,mf,ar,wr] = Construction(arch,W,score) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + [~,n] = size(arch); + [N ,~] = size(W); + if size(arch,2) > size(W,1) + a(N+1).r = []; + de = arch.decs; + ma = arch.masks; + m = arch.Gns; + A_sp = zeros(1,n); + Sp = zeros(1,n); + for i = 1 : n + a(arch(i).tno).r = [a(arch(i).tno).r,i]; + sp = sum(arch(i).mobj,2); + sp_b = min(sp); + A_sp(1,i) = mean(sum(abs(sp - sp_b))); + A_sp(1,i) = 1/((sum(ma(i,:)&(score>0.5)))/size(score,2)); + Sp(1,i) = mean(sp); + end + rch = []; + w = []; + j = 0; + for r = 1 : N + if a(r).r > 0 + [~,c] = size(a(r).r); + s = zeros(1,c); + t = zeros(1,c); + gn = 0; + for d = 1 : c + gn = gn + a(r).r(1,d); + end + gn = gn/c; + for d = 1 : c + no = a(r).r(1,d); + if( m(no)>gn ) + T = 0; + else + T = gn-m(no); + end + s(1,d) = Sp(1, no)+(A_sp(1,no)); + t(1,d) = no; + end + [~,b] = sort(s(1,:)); + j = j + 1; + tt = t(1,b(1,1)); + RF(j,:) = de(t(1,b(1,1)),:); + MF(j,:) = ma(t(1,b(1,1)),:); + rch = [rch,tt]; + w = [w,r]; + end + end + end + ar = rch; + wr = w; + rf = RF; + mf = MF; +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/EnvironmentalSelection.m b/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/EnvironmentalSelection.m new file mode 100644 index 000000000..f236c66a1 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/EnvironmentalSelection.m @@ -0,0 +1,38 @@ +function [Population,Dec,Mask,FrontNo,CrowdDis] = EnvironmentalSelection(Population,Dec,Mask,N) +% Environmental selection + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + %% Delete duplicated solutions + [~,uni] = unique(Population.objs,'rows'); + Population = Population(uni); + Dec = Dec(uni,:); + Mask = Mask(uni,:); + N = min(N,length(Population)); + + %% Non-dominated sorting + [FrontNo,MaxFNo] = NDSort(Population.objs,Population.cons,N); + Next = FrontNo < MaxFNo; + + %% Calculate the crowding distance of each solution + CrowdDis = CrowdingDistance(Population.objs,FrontNo); + + %% Select the solutions in the last front based on their crowding distances + Last = find(FrontNo==MaxFNo); + [~,Rank] = sort(CrowdDis(Last),'descend'); + Next(Last(Rank(1:N-sum(Next)))) = true; + + %% Population for next generation + Population = Population(Next); + FrontNo = FrontNo(Next); + CrowdDis = CrowdDis(Next); + Dec = Dec(Next,:); + Mask = Mask(Next,:); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/Final.m b/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/Final.m new file mode 100644 index 000000000..00bc3699d --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/Final.m @@ -0,0 +1,47 @@ +function [Population] = Final(Problem,Arch,N,W,score) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + if length(Arch) <= N + Population = Problem.Evaluation(Arch.decs.*Arch.masks); + else + RF = []; + MF = []; + while size(W,1) > 0 + [srch,W] = assign(Arch,W); + [rf,mf,rch,wr] = Construction(srch,W,score); + RF = [rf;RF]; + MF = [mf;MF]; + Arch(rch) = []; + W(wr,:) = []; + end + Population = Problem.Evaluation(RF.*MF); + end +end + +function [ar,w] = assign(arch,W) + Popobj = arch.objs; + L = max(Popobj)-min(Popobj); + objs = (Popobj-min(Popobj))./L ; + [~,n] = size(arch); + [N ,~] = size(W); + for x = 1 : n + obj = objs(x,:); + for y = 1 : N + s = sum(W(y,:).*obj,2); + m = sqrt(sum(W(y,:).*W(y,:),2)*sum(obj.*obj,2)); + dang(1,y) = acos(s/m); + [~,h] = sort(dang); + end + arch(x).tno = h(1); + end + w = W; + ar = arch; +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/FitnessCal.m b/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/FitnessCal.m new file mode 100644 index 000000000..71a1f2e8c --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/FitnessCal.m @@ -0,0 +1,30 @@ +function[score] = FitnessCal(Mask,FrontNo,score) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + [N,d] = size(Mask); + Fit = zeros(1,d); + b = 0; + h = 0; + for i = 1 : N + h = h + 1; + if FrontNo(h) <= 1 + b = b + 1; + a = 1; + for j = 1 : d + fit = 1/(2+sqrt(N)*Mask(i,j)); + Fit(1,a) = fit ; + a = a + 1; + end + score = score + Fit; + end + end + score = score./b; +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/Initialization.m b/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/Initialization.m new file mode 100644 index 000000000..e6291effa --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/Initialization.m @@ -0,0 +1,18 @@ +function [Arch,score] = Initialization(Population,Dec,Mask,FrontNo) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + [Next] = find(FrontNo==1); + Population = Population(Next).objs; + Dec = Dec(Next,:); + Mask = Mask(Next,:); + score = sum(Mask,1)/size(Mask,1); + Arch = archives(Population,Dec,Mask); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/LRMOEA.m b/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/LRMOEA.m new file mode 100644 index 000000000..eecaa95d1 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/LRMOEA.m @@ -0,0 +1,57 @@ +classdef LRMOEA < ALGORITHM +% +% Large-scale robust multi-objective evolutionary algorithm + +%------------------------------- Reference -------------------------------- +% S. Shao, Y. Tian, L. Zhang, K. C. Tan, and X. Zhang, An evolutionary +% algorithm for solving large-scale robust multi-objective optimization +% problems, IEEE Transactions on Evolutionary Computation, 2024. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + methods + function main(Algorithm,Problem) + %% Parameter setting + [thea,eta] = Algorithm.ParameterSet(0.2,1.15); + W = UniformPoint(Problem.N,Problem.M); + if Problem.encoding(1)==4 + Dec = ones(Problem.N,Problem.D); + else + Dec = unifrnd(repmat(Problem.lower,Problem.N,1),repmat(Problem.upper,Problem.N,1)); + end + Mask = false(size(Dec)); + for i = 1 : Problem.N + Mask(i,randperm(end,ceil(rand.^2*end))) = true; + end + Population = Problem.Evaluation(Dec.*Mask); + [Population,Dec,Mask,FrontNo,CrowdDis] = EnvironmentalSelection(Population,Dec,Mask,Problem.N); + obj =Population.objs; + score = ones(1,Problem.D); + score = FitnessCal(Mask,FrontNo,score); + Arch = archives(obj,Dec,Mask); + + %% Optimization + while Algorithm.NotTerminated(Population) + MatingPool = TournamentSelection(2,2*Problem.N,FrontNo,-CrowdDis); + [OffDec,OffMask] = LROperator(Problem,Dec(MatingPool,:),Mask(MatingPool,:),score); + Offspring = Problem.Evaluation(OffDec.*OffMask); + [Population,Dec,Mask,FrontNo,CrowdDis] = EnvironmentalSelection([Population,Offspring],[Dec;OffDec],[Mask;OffMask],Problem.N); + score = sum(Arch.masks)/size(Arch,2); + if size(score,2) == 1 + score = ones(1,Problem.D); + end + TemArch = Initialization(Population,Dec,Mask,FrontNo); + Arch = ArchUpdate(Problem,Arch,TemArch,thea,eta); + if Problem.FE >= Problem.maxFE + Population = Final(Problem,Arch,Problem.N,W,score); + end + end + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/LROperator.m b/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/LROperator.m new file mode 100644 index 000000000..e0f2e4077 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/LROperator.m @@ -0,0 +1,62 @@ +function [OffDec,OffMask] = LROperator(Problem,ParentDec,ParentMask,Fitness) +% Operator + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + %% Parameter setting + [N,D] = size(ParentDec); + Parent1Mask = ParentMask(1:N/2,:); + Parent2Mask = ParentMask(N/2+1:end,:); + + %% Crossover for mask + OffMask = Parent1Mask; + for i = 1 : N/2 + if rand < 0.5 + index = find(Parent1Mask(i,:)&~Parent2Mask(i,:)); + index = index(TS(Fitness(index))); + OffMask(i,index) = 0; + else + index = find(~Parent1Mask(i,:)&Parent2Mask(i,:)); + index = index(TS(-Fitness(index))); + OffMask(i,index) = Parent2Mask(i,index); + end + end + + %% Mutation for mask + for i = 1 : N/2 + if rand < 0.5 + index = find(OffMask(i,:)); + index = index(TS(Fitness(index))); + OffMask(i,index) = 0; + else + index = find(~OffMask(i,:)); + index = index(TS(-Fitness(index))); + OffMask(i,index) = 1; + end + end + + %% Crossover and mutation for dec + if any(Problem.encoding~=4) + OffDec = OperatorGAhalf(Problem,ParentDec); + OffDec(:,Problem.encoding==4) = 1; + else + OffDec = ones(N/2,D); + end +end + +function index = TS(Fitness) +% Binary tournament selection + + if isempty(Fitness) + index = []; + else + index = TournamentSelection(2,1,Fitness); + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/archives.m b/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/archives.m new file mode 100644 index 000000000..fbd72770c --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/LRMOEA/archives.m @@ -0,0 +1,55 @@ +classdef archives < handle + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + properties + dec; + mask; + obj; + mobj; + mr; + tno; + Gn; + end + methods + function [Arch] = archives(Pop,Dec,Mask) + if nargin > 0 + Arch(1,size(Pop,1)) = Arch; + for i = 1 : size(Pop,1) + Arch(i).dec = Dec(i,:); + Arch(i).mask = Mask(i,:); + Arch(i).obj = Pop(i,:); + Arch(i).mobj = Pop(i,:); + Arch(i).mr = 0; + Arch(i).tno = 0; + Arch(i).Gn = 1; + end + end + end + function value = decs(Arch) + value = cat(1,Arch.dec); + end + function value = objs(Arch) + value = cat(1,Arch.obj); + end + function value = masks(Arch) + value = cat(1,Arch.mask); + end + function value = mrs(Arch) + value = cat(1,Arch.mr); + end + function value = tnos(Arch) + value = cat(1,Arch.tno); + end + function value =Gns(Arch) + value = cat(1,Arch.Gn); + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/MOBCA/MOBCA.m b/PlatEMO/Algorithms/Multi-objective optimization/MOBCA/MOBCA.m index 982905d29..909a82948 100644 --- a/PlatEMO/Algorithms/Multi-objective optimization/MOBCA/MOBCA.m +++ b/PlatEMO/Algorithms/Multi-objective optimization/MOBCA/MOBCA.m @@ -1,6 +1,6 @@ classdef MOBCA < ALGORITHM % -% Multi-objective besiege and conquer alogrithm +% Multi-objective besiege and conquer algorithm % BCB --- 0.2 --- Set BCB % nSoldiers --- 3 --- Number of soldiers for each armies % div --- 10 --- Division number of grids diff --git a/PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/BinaryCrossover.m b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/BinaryCrossover.m new file mode 100644 index 000000000..f6e451b1b --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/BinaryCrossover.m @@ -0,0 +1,24 @@ +function Offspring = BinaryCrossover(Parent1,Parent2) +% Unbalanced binary crossover + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2021 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + Offspring = logical(Parent1); + for i = 1 : size(Offspring,1) + diff = find(Parent1(i,:)~=Parent2(i,:)); + MOne = mean(Offspring(i,diff)); + r = min(min(0.5,2*MOne),2*(1-MOne)); + rate = zeros(1,length(diff)); + rate(Offspring(i,diff)) = r/2/MOne; + rate(~Offspring(i,diff)) = r/2/(1-MOne); + exchange = rand(1,length(diff)) < rate; + Offspring(i,diff(exchange)) = ~Offspring(i,diff(exchange)); + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/BinaryMutation.m b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/BinaryMutation.m new file mode 100644 index 000000000..b2c3ad003 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/BinaryMutation.m @@ -0,0 +1,23 @@ +function Offspring = BinaryMutation(Offspring) +% Unbalanced binary mutation + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2021 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + [N,D] = size(Offspring); + MOne = mean(Offspring,2); + r = min(min(1/D,2*MOne),2*(1-MOne)); + rate1 = repmat(r./2./MOne,1,D); + rate0 = repmat(r./2./(1-MOne),1,D); + rate = zeros(N,D); + rate(Offspring) = rate1(Offspring); + rate(~Offspring) = rate0(~Offspring); + exchange = rand(N,D) < rate; + Offspring(exchange) = ~Offspring(exchange); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/EnvironmentalSelection.m b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/EnvironmentalSelection.m new file mode 100644 index 000000000..43fe7ef64 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/EnvironmentalSelection.m @@ -0,0 +1,63 @@ +function [Population,Dec,Mask,sRatio] = EnvironmentalSelection(Population,Dec,Mask,N,len,num) +% The environmental selection of SPEA2 + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + %% Delete duplicated solutions + success = false(1,length(Population)); + [~,uni] = unique(Population.objs,'rows'); + if length(uni) == 1 + [~,uni] = unique(Population.decs,'rows'); + end + Population = Population(uni); + Dec = Dec(uni,:); + Mask = Mask(uni,:); + N = min(N,length(Population)); + + %% Calculate the fitness of each solution + [FrontNo,MaxFNo] = NDSort(Population.objs,N); + Next = false(1,length(FrontNo)); + Next(FrontNo +% Multi-objective evolutionary algorithm based on cross-scale knowledge fusion + +%------------------------------- Reference -------------------------------- +% Z. Ding, L. Chen, D. Sun, X. and Zhang, Efficient sparse large-scale +% multi-objective optimization based on cross-scale knowledge fusion, +% IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2024. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Lei Chen (email: soarfx@163.com) + + methods + function main(Algorithm,Problem) + %% Population initialization + REAL = ~strcmp(Problem.encoding,'binary'); + [Dec,Mask,Population,Fitness]=PriorAnalysis_initialization(Problem,REAL); + [Population,Dec,Mask] = EnvironmentalSelection(Population,Dec,Mask,Problem.N,0,0); + rho = 0.5; + GROUP = []; % In order to prevent outliers in Kmeans algorithm + + %% Optimization + while Algorithm.NotTerminated(Population) + [FrontNo,~] = NDSort(Population.objs,Population.cons,inf); + CrowdDis = CrowdingDistance(Population.objs,FrontNo); + % Divide the population, Pop1 is used as the population for + % dual dimension reduction to generate offspring, and Pop2 + % is used as the population of dual grouping. + Pop1_Site = rho > rand(1,length(Population)); + if sum(Pop1_Site) >= 2 + Pop1 = Population(Pop1_Site); + Dec1 = Dec(Pop1_Site,:); + Mask1 = Mask(Pop1_Site,:); + Pop2 = Population(~Pop1_Site); + Dec2 = Dec(~Pop1_Site,:); + Mask2 = Mask(~Pop1_Site,:); + elseif sum(~Pop1_Site) < 1 + Pop1 = Population; + Dec1 = Dec; + Mask1 = Mask; + [Pop2,Dec2,Mask2] = deal([]); + else + [Pop1,Dec1,Mask1] = deal([]); + Pop2 = Population; + Dec2 = Dec; + Mask2 = Mask; + end + % decision variable Sparsity analysis + [Local_Knowlege,Global_Knowledge,Fitness,NSV,SV,theta] = SparsityAnalysis(Problem,Mask,FrontNo,Fitness,GROUP); + GROUP = [NSV;SV]; + % dual dimension reduction + if ~isempty(Pop1) + [OffDec1,OffMask1,len1] = Reproduction1(Problem,Pop1,Dec1,Mask1,FrontNo,CrowdDis,Pop1_Site,Local_Knowlege,Global_Knowledge,NSV,SV,theta,REAL); + Offspring1 = Problem.Evaluation(OffDec1.*OffMask1); + else + [OffDec1,OffMask1,Offspring1] = deal([]); + len1 = 0; + end + % dual grouping + if ~isempty(Pop2) + MatingPool = TournamentSelection(2,(Problem.N-len1)*2,FrontNo(~Pop1_Site),-CrowdDis(~Pop1_Site)); + [OffDec2,OffMask2] = Reproduction2(Problem,Dec2(MatingPool,:),Mask2(MatingPool,:),SV,NSV,REAL); + Offspring2 = Problem.Evaluation(OffDec2.*OffMask2); + else + [OffDec2,OffMask2,Offspring2] = deal([]); + end + [Population,Dec,Mask,sRatio] = EnvironmentalSelection([Population,Offspring1,Offspring2],[Dec;OffDec1;OffDec2],[Mask;OffMask1;OffMask2],Problem.N,length(Population),len1); + rho = (rho+sRatio)/2; + end + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/PriorAnalysis_initialization.m b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/PriorAnalysis_initialization.m new file mode 100644 index 000000000..af154982c --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/PriorAnalysis_initialization.m @@ -0,0 +1,44 @@ +function [TDec,TMask,TempPop,Fitness]=PriorAnalysis_initialization(Problem,REAL) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + TDec = []; + TMask = []; + TempPop = []; + Fitness = zeros(1,Problem.D); + for i = 1 : 1+4*REAL + if REAL + Dec = unifrnd(repmat(Problem.lower,Problem.D,1),repmat(Problem.upper,Problem.D,1)); + else + Dec = ones(Problem.D,Problem.D); + end + Mask = eye(Problem.D); + Population = Problem.Evaluation(Dec.*Mask); + TDec = [TDec;Dec]; + TMask = [TMask;Mask]; + TempPop = [TempPop,Population]; + Fitness = Fitness + NDSort([Population.objs,Population.cons],inf); + end + + % Generate initial population + if REAL + Dec = unifrnd(repmat(Problem.lower,Problem.N,1),repmat(Problem.upper,Problem.N,1)); + else + Dec = ones(Problem.N,Problem.D); + end + Mask = zeros(Problem.N,Problem.D); + for i = 1 : Problem.N + Mask(i,TournamentSelection(2,ceil(rand*Problem.D),Fitness)) = 1; + end + Population = Problem.Evaluation(Dec.*Mask); + TDec = [TDec;Dec]; + TMask = [TMask;Mask]; + TempPop = [TempPop,Population]; +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/Reproduction1.m b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/Reproduction1.m new file mode 100644 index 000000000..bef9da601 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/Reproduction1.m @@ -0,0 +1,145 @@ +function [OffDec,OffMask,len1]=Reproduction1(Problem,Pop1,Dec1,Mask1,FrontNo,CrowdDis,Pop1_Site,Local_Knowlege,Global_Knowlege,NSV,SV,theta,REAL) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + len1 = length(Pop1); + MatingPool = TournamentSelection(2,len1*2,FrontNo(Pop1_Site),-CrowdDis(Pop1_Site)); + + % Mask evolution of offspring + Parent1Mask = Mask1(MatingPool(1:end/2),:); + Parent2Mask = Mask1(MatingPool((end/2+1):end),:); + [N,D] = size(Parent1Mask); + + OffMask = false(N,D); + + for i = 1 : N + if rand < theta + allOne = Local_Knowlege(1,:); + other = Local_Knowlege(3,:); + TOffMask = false(1,D); + TOffMask(other(NSV)) = BinaryCrossover(Parent1Mask(i,other(NSV)),Parent2Mask(i,other(NSV))); + TOffMask(other(NSV)) = BinaryMutation(TOffMask(other(NSV))); + + TOffMask(other(SV)) = BinaryCrossover(Parent1Mask(i,other(SV)),Parent2Mask(i,other(SV))); + TOffMask(other(SV)) = BinaryMutation(TOffMask(other(SV))); + TOffMask(allOne) = true; + else + allOne = Global_Knowlege(1,:); + other = Global_Knowlege(3,:); + TOffMask = false(1,D); + TOffMask(other(NSV)) = BinaryCrossover(Parent1Mask(i,other(NSV)),Parent2Mask(i,other(NSV))); + TOffMask(other(NSV)) = BinaryMutation(TOffMask(other(NSV))); + + TOffMask(other(SV)) = BinaryCrossover(Parent1Mask(i,other(SV)),Parent2Mask(i,other(SV))); + TOffMask(other(SV)) = BinaryMutation(TOffMask(other(SV))); + TOffMask(allOne) = true; + end + OffMask(i,:) = TOffMask; + end + + if REAL + allOne = Global_Knowlege(1,:); + allZero = Global_Knowlege(2,:); + other = Global_Knowlege(3,:); + % data preprocessing + T_Best_Dec = Dec1(:,~allZero); + VariableMean = mean(T_Best_Dec,1); + VariableNormlize = max(T_Best_Dec)-min(T_Best_Dec)+1e-6; + Temp_PopDec = (T_Best_Dec-repmat(VariableMean,N,1))./repmat(VariableNormlize,N,1); + % SVD decomposition + [U,~,~] = svd((1/N).*(Temp_PopDec'*Temp_PopDec)); + % Guide subspace size + K = sum(other(NSV)) + sum(allOne); + + U_reduce = U(:,1:K); + Pop_reduce = Temp_PopDec*U_reduce; + OffDec_reduce = GAhalfCross(Pop_reduce(MatingPool,:)); + % Restore to original space + T_OffDec = OffDec_reduce*U_reduce'; + T_OffDec = (T_OffDec.*repmat(VariableNormlize,N,1))+repmat(VariableMean,N,1); + % Perform polynomial variation + T_OffDec = PM(Problem,T_OffDec,~allZero); + % to reduce the computational complexity of PCA + if sum(allZero) > 0 + OffDec = zeros(N,D); + OffDec(:,allZero) = GAhalf(Problem,Dec1(MatingPool,allZero),allZero); + OffDec(:,~allZero) = T_OffDec; + else + OffDec = T_OffDec; + end + else + OffDec = ones(N,D); + end +end + +function Offspring = PM(Problem,Offspring,site) + [~,~,proM,disM] = deal(1,20,1,20); + [N,D] = size(Offspring); + Lower = repmat(Problem.lower,N,1); + Upper = repmat(Problem.upper,N,1); + Lower = Lower(:,site); + Upper = Upper(:,site); + + Site = rand(N,D) < proM/D; + mu = rand(N,D); + temp = Site & mu<=0.5; + Offspring = min(max(Offspring,Lower),Upper); + Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*((2.*mu(temp)+(1-2.*mu(temp)).*... + (1-(Offspring(temp)-Lower(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))-1); + temp = Site & mu>0.5; + Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*(1-(2.*(1-mu(temp))+2.*(mu(temp)-0.5).*... + (1-(Upper(temp)-Offspring(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))); +end + +function Offspring = GAhalfCross(Parent) + [proC,disC,~,~] = deal(1,20,1,20); + Parent1 = Parent(1:floor(end/2),:); + Parent2 = Parent(floor(end/2)+1:floor(end/2)*2,:); + [N,D] = size(Parent1); + beta = zeros(N,D); + mu = rand(N,D); + beta(mu<=0.5) = (2*mu(mu<=0.5)).^(1/(disC+1)); + beta(mu>0.5) = (2-2*mu(mu>0.5)).^(-1/(disC+1)); + beta = beta.*(-1).^randi([0,1],N,D); + beta(rand(N,D)<0.5) = 1; + beta(repmat(rand(N,1)>proC,1,D)) = 1; + Offspring = (Parent1+Parent2)/2+beta.*(Parent1-Parent2)/2; +end + +function Offspring = GAhalf(Problem,Parent,allZero) + [proC,disC,proM,disM] = deal(1,20,1,20); + Parent1 = Parent(1:floor(end/2),:); + Parent2 = Parent(floor(end/2)+1:floor(end/2)*2,:); + [N,D] = size(Parent1); + + beta = zeros(N,D); + mu = rand(N,D); + beta(mu<=0.5) = (2*mu(mu<=0.5)).^(1/(disC+1)); + beta(mu>0.5) = (2-2*mu(mu>0.5)).^(-1/(disC+1)); + beta = beta.*(-1).^randi([0,1],N,D); + beta(rand(N,D)<0.5) = 1; + beta(repmat(rand(N,1)>proC,1,D)) = 1; + Offspring = (Parent1+Parent2)/2+beta.*(Parent1-Parent2)/2; + + Lower = repmat(Problem.lower,N,1); + Upper = repmat(Problem.upper,N,1); + Lower = Lower(:,allZero); + Upper = Upper(:,allZero); + + Site = rand(N,D) < proM/D; + mu = rand(N,D); + temp = Site & mu<=0.5; + Offspring = min(max(Offspring,Lower),Upper); + Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*((2.*mu(temp)+(1-2.*mu(temp)).*... + (1-(Offspring(temp)-Lower(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))-1); + temp = Site & mu>0.5; + Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*(1-(2.*(1-mu(temp))+2.*(mu(temp)-0.5).*... + (1-(Upper(temp)-Offspring(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/Reproduction2.m b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/Reproduction2.m new file mode 100644 index 000000000..3db1926cf --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/Reproduction2.m @@ -0,0 +1,71 @@ +function [OffDec,OffMask] = Reproduction2(Problem,ParentDec,ParentMask,NSV,SV,REAL) +% The operator of MOEA/CKF + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + %% Parameter setting + Parent1Mask = ParentMask(1:end/2,:); + Parent2Mask = ParentMask(end/2+1:end,:); + [N,D] = size(Parent1Mask); + + %% Binary variation + OffMask = false(N,D); + OffMask(:,SV) = BinaryCrossover(Parent1Mask(:,SV),Parent2Mask(:,SV)); + OffMask(:,SV) = BinaryMutation(OffMask(:,SV)); + + OffMask(:,NSV) = BinaryCrossover(Parent1Mask(:,NSV),Parent2Mask(:,NSV)); + OffMask(:,NSV) = BinaryMutation(OffMask(:,NSV)); + + %% Real variation + if REAL + Parent1 = ParentDec(1:floor(end/2),:); + Parent2 = ParentDec(floor(end/2)+1:floor(end/2)*2,:); + OffDec = zeros(N,D); + for i = 1 : N + NonZero = OffMask(i,:); + OffDec(i,NonZero) = SBXhalf(Parent1(i,NonZero),Parent2(i,NonZero)); + OffDec(i,NonZero) = PM(Problem,OffDec(i,NonZero),NonZero); + OffDec(i,~NonZero) = SBXhalf(Parent1(i,~NonZero),Parent2(i,~NonZero)); + OffDec(i,~NonZero) = PM(Problem,OffDec(i,~NonZero),~NonZero); + end + else + OffDec = ones(size(OffMask)); + end +end + +function Offspring = SBXhalf(Parent1,Parent2) + [proC,disC,~,~] = deal(1,20,1,20); + [N,D] = size(Parent1); + beta = zeros(N,D); + mu = rand(N,D); + beta(mu<=0.5) = (2*mu(mu<=0.5)).^(1/(disC+1)); + beta(mu>0.5) = (2-2*mu(mu>0.5)).^(-1/(disC+1)); + beta = beta.*(-1).^randi([0,1],N,D); + beta(rand(N,D)<0.5) = 1; + beta(repmat(rand(N,1)>proC,1,D)) = 1; + Offspring = (Parent1+Parent2)/2+beta.*(Parent1-Parent2)/2; +end + +function Offspring = PM(Problem,Offspring,indexSite) + [N,D] = size(Offspring); + [proM,disM] = deal(1,20); + % Polynomial mutation + Lower = repmat(Problem.lower(indexSite),N,1); + Upper = repmat(Problem.upper(indexSite),N,1); + Site = rand(N,D) < proM/D; + mu = rand(N,D); + temp = Site & mu<=0.5; + Offspring = min(max(Offspring,Lower),Upper); + Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*((2.*mu(temp)+(1-2.*mu(temp)).*... + (1-(Offspring(temp)-Lower(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))-1); + temp = Site & mu>0.5; + Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*(1-(2.*(1-mu(temp))+2.*(mu(temp)-0.5).*... + (1-(Upper(temp)-Offspring(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))); + end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/SparsityAnalysis.m b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/SparsityAnalysis.m new file mode 100644 index 000000000..58e7325fe --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-CKF/SparsityAnalysis.m @@ -0,0 +1,40 @@ +function [Local_Knowlege,Gobal_Knowlege,Fitness,NSV,SV,theta]=SparsityAnalysis(Problem,Mask,FrontNo,Fitness,GROUP) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + Local_Knowlege = false(3,Problem.D); + Gobal_Knowlege = false(3,Problem.D); + EliteMask = Mask(FrontNo==1,:); + Local_Knowlege(1,:) = all(EliteMask,1); + Local_Knowlege(2,:) = all(~EliteMask,1); + Local_Knowlege(3,:) = ~ Local_Knowlege(1,:) & ~ Local_Knowlege(2,:); + + Gobal_Knowlege(1,:) = all(Mask,1); + Gobal_Knowlege(2,:) = all(~Mask,1); + Gobal_Knowlege(3,:) = ~ Gobal_Knowlege(1,:) & ~ Gobal_Knowlege(2,:); + + theta = size(EliteMask,1)/size(Mask,1); + Fitness = (Fitness-min(Fitness)+1e-6)./(max(Fitness)-min(Fitness)+1e-6)+1e-6; + + if size(Fitness',1) < 2 + NSV = GROUP(1,:); + SV = GROUP(2,:); + else + Cluster = kmeans(Fitness',2); + Fitness = Fitness + (1-mean(EliteMask)).*(0.5*theta+0.5*(Problem.FE/Problem.maxFE)).*Fitness; + if mean(Fitness(Cluster==1)) > mean(Fitness(Cluster==2)) + NSV = Cluster == 2; + SV = Cluster == 1; + else + NSV = Cluster == 1; + SV = Cluster == 2; + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/MOEA-NZD/DimJud.m b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-NZD/DimJud.m new file mode 100644 index 000000000..4387afb39 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-NZD/DimJud.m @@ -0,0 +1,55 @@ +function [Re, Population] = DimJud(Population, upper, lower,ReAlready) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Xiangyu Wang (email: xiangyu.wang@uni-bielefeld.de) + + [N1,N2] = size(Population); + density = mean(Population~=0,1); + for i = 1 : N2 + dim_Q75(i) = prctile(Population(Population(:,i)~=0,i),75); + dim_Q50(i) = prctile(Population(Population(:,i)~=0,i),50); + dim_Q25(i) = prctile(Population(Population(:,i)~=0,i),25); + end + dim_Q75(isnan(dim_Q75)) = 0; + dim_Q50(isnan(dim_Q50)) = 0; + dim_Q25(isnan(dim_Q25)) = 0; + km_input = zeros(1,N2); + upper_index = dim_Q50>0; + km_input(upper_index) = dim_Q75(upper_index)./upper(upper_index); + lower_index = dim_Q50<0; + km_input(lower_index) = dim_Q25(lower_index)./lower(lower_index); + km_input = sqrt(1-(km_input-1).^2); + [C_indx,C_point] = ExactKmeans([km_input',density'],4); + [~,a] = max(C_point(:,1)+C_point(:,2)); + activating_index = C_indx == a; + + %% Case 1: dim_mean > 0 + ReNow = false(1,size(ReAlready,2)); + upper_index1 = upper_index + activating_index; + ReNow(upper_index1==2) = true; + ReNow(ReAlready == true) = false; + range = upper(ReNow) - dim_Q75(ReNow); + if ~isempty(range) + Population(:,ReNow) = rand(N1,size(range,2)).*range + repmat(dim_Q75(ReNow),N1,1); + end + ReAlready(ReNow ==true) = true; + + %% Case 2: dim_mean < 0 + upper_index1 = lower_index + activating_index; + ReNow(upper_index1==2) = true; + ReNow(ReAlready ==true) = false; + range = dim_Q25(ReNow) - lower(ReNow); + if ~isempty(range) + Population(:,ReNow) = -1.*rand(N1,size(range,2)).*range + repmat(dim_Q25(ReNow),N1,1); + end + ReAlready(ReNow ==true) = true; + Re = ReAlready; +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/MOEA-NZD/DimJud0.m b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-NZD/DimJud0.m new file mode 100644 index 000000000..9b1e95c3d --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-NZD/DimJud0.m @@ -0,0 +1,47 @@ +function [Parent, Non0_index]= DimJud0(Parent,Problem) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Xiangyu Wang (email: xiangyu.wang@uni-bielefeld.de) + + %% feature extraction + [~,N2] = size(Parent); + % density + density = mean(Parent~=0,1); + % calculate the statistics + for i = 1: N2 + dim_Q75(i) = prctile(Parent(Parent(:,i)~=0,i),75); + dim_Q50(i) = prctile(Parent(Parent(:,i)~=0,i),50); + dim_Q25(i) = prctile(Parent(Parent(:,i)~=0,i),25); + end + dim_Q75(isnan(dim_Q75)) = 0; + dim_Q50(isnan(dim_Q50)) = 0; + dim_Q25(isnan(dim_Q25)) = 0; + + % Normalization + ParentN = zeros(1,N2); + % >0 + upper_index = dim_Q50>0; + ParentN(upper_index) = dim_Q75(upper_index)./Problem.upper(upper_index); + % <0 + lower_index = dim_Q50<0; + ParentN(lower_index) = dim_Q25(lower_index)./Problem.lower(lower_index); + % projection + ParentN = sqrt(1-(ParentN-1).^2); + % ClusteringS + [C_indx,C_point] = ExactKmeans([ParentN',density'],4); + [~,a] = sort(C_point(:,1)); + Is0_index1 = C_indx == a(1); + Is0_index2 = C_indx == a(2); + Is0_index = (Is0_index1 + Is0_index2) == 1; + Non0_index = 1-Is0_index; + % setting 0 + Parent(:, Is0_index) = 0; +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/MOEA-NZD/EnvironmentalSelection.m b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-NZD/EnvironmentalSelection.m new file mode 100644 index 000000000..b981dee1a --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-NZD/EnvironmentalSelection.m @@ -0,0 +1,88 @@ +function Population = EnvironmentalSelection(Population,N,Z,Zmin) +% The environmental selection of NSGA-III + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + if isempty(Zmin) + Zmin = ones(1,size(Z,2)); + end + + %% Non-dominated sorting + [FrontNo,MaxFNo] = NDSort(Population.objs,Population.cons,N); + Next = FrontNo < MaxFNo; + + %% Select the solutions in the last front + Last = find(FrontNo==MaxFNo); + Choose = LastSelection(Population(Next).objs,Population(Last).objs,N-sum(Next),Z,Zmin); + Next(Last(Choose)) = true; + % Population for next generation + Population = Population(Next); +end + +function Choose = LastSelection(PopObj1,PopObj2,K,Z,Zmin) +% Select part of the solutions in the last front + + PopObj = [PopObj1;PopObj2] - repmat(Zmin,size(PopObj1,1)+size(PopObj2,1),1); + [N,M] = size(PopObj); + N1 = size(PopObj1,1); + N2 = size(PopObj2,1); + NZ = size(Z,1); + + %% Normalization + % Detect the extreme points + Extreme = zeros(1,M); + w = zeros(M)+1e-6+eye(M); + for i = 1 : M + [~,Extreme(i)] = min(max(PopObj./repmat(w(i,:),N,1),[],2)); + end + % Calculate the intercepts of the hyperplane constructed by the extreme + % points and the axes + Hyperplane = PopObj(Extreme,:)\ones(M,1); + a = 1./Hyperplane; + if any(isnan(a)) + a = max(PopObj,[],1)'; + end + % Normalization + PopObj = PopObj./repmat(a',N,1); + + %% Associate each solution with one reference point + % Calculate the distance of each solution to each reference vector + Cosine = 1 - pdist2(PopObj,Z,'cosine'); + Distance = repmat(sqrt(sum(PopObj.^2,2)),1,NZ).*sqrt(1-Cosine.^2); + % Associate each solution with its nearest reference point + [d,pi] = min(Distance',[],1); + + %% Calculate the number of associated solutions except for the last front of each reference point + rho = hist(pi(1:N1),1:NZ); + + %% Environmental selection + Choose = false(1,N2); + Zchoose = true(1,NZ); + % Select K solutions one by one + while sum(Choose) < K + % Select the least crowded reference point + Temp = find(Zchoose); + Jmin = find(rho(Temp)==min(rho(Temp))); + j = Temp(Jmin(randi(length(Jmin)))); + I = find(Choose==0 & pi(N1+1:end)==j); + % Then select one solution associated with this reference point + if ~isempty(I) + if rho(j) == 0 + [~,s] = min(d(N1+I)); + else + s = randi(length(I)); + end + Choose(I(s)) = true; + rho(j) = rho(j) + 1; + else + Zchoose(j) = false; + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/MOEA-NZD/ExactKmeans.m b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-NZD/ExactKmeans.m new file mode 100644 index 000000000..a60ec74ca --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-NZD/ExactKmeans.m @@ -0,0 +1,46 @@ +function [c_indx,c_point] = ExactKmeans(feature,K) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Xiangyu Wang (email: xiangyu.wang@uni-bielefeld.de) + + % ClusteringS + N1 = size(feature,1); + % Finding the max and min value + [f_max] = max(feature); + [f_min] = min(feature); + A1 = [f_max(1),f_max(2)]; + B1 = [f_max(1),f_min(2)]; + C1 = [f_min(1),f_max(2)]; + D1 = [f_min(1),f_min(2)]; + A = A1; + B = B1; + C = C1; + D = D1; + for i = 1 : 100 + dis(:,1) = sqrt((feature(:,1)-A(1)).^2+(feature(:,2)-A(2)).^2); + dis(:,2) = sqrt((feature(:,1)-B(1)).^2+(feature(:,2)-B(2)).^2); + dis(:,3) = sqrt((feature(:,1)-C(1)).^2+(feature(:,2)-C(2)).^2); + dis(:,4) = sqrt((feature(:,1)-D(1)).^2+(feature(:,2)-D(2)).^2); + for j = 1:N1 + [~,indx(j)] = min(dis(j,:)); + end + A = mean(feature(indx==1,:),1); + B = mean(feature(indx==2,:),1); + C = mean(feature(indx==3,:),1); + D = mean(feature(indx==4,:),1); + A(isnan(A)) = A1(isnan(A)); + B(isnan(B)) = B1(isnan(B)); + C(isnan(C)) = C1(isnan(C)); + D(isnan(D)) = D1(isnan(D)); + end + c_indx = indx; + c_point = [A;B;C;D]; +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/MOEA-NZD/GA0.m b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-NZD/GA0.m new file mode 100644 index 000000000..060136a73 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-NZD/GA0.m @@ -0,0 +1,142 @@ +function Offspring = GA0(Parent,Non0_index,Problem,Parameter) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Xiangyu Wang (email: xiangyu.wang@uni-bielefeld.de) + + %% Parameter setting + if nargin > 3 + [proC,disC,proM,disM] = deal(Parameter{:}); + else + [proC,disC,proM,disM] = deal(1,20,1,20); + end + if isa(Parent(1),'SOLUTION') + evaluated = true; + Parent = Parent.decs; + else + evaluated = false; + end + + Parent1 = Parent(1:floor(end/2),:); + Parent2 = Parent(floor(end/2)+1:floor(end/2)*2,:); + Type = arrayfun(@(i)find(Problem.encoding==i),1:5,'UniformOutput',false); + if ~isempty([Type{1:2}]) % Real and integer variables + Offspring(:,[Type{1:2}]) = GAreal(Parent1(:,[Type{1:2}]),Parent2(:,[Type{1:2}]),Problem.lower([Type{1:2}]),Problem.upper([Type{1:2}]),proC,disC,proM*length([Type{1:2}])/size(Parent1,2),disM, Non0_index); + end + if ~isempty(Type{3}) % Label variables + Offspring(:,Type{3}) = GAlabel(Parent1(:,Type{3}),Parent2(:,Type{3}),Problem.lower(Type{3}),Problem.upper(Type{3}),proC,proM*length(Type{3})/size(Parent1,2)); + end + if ~isempty(Type{4}) % Binary variables + Offspring(:,Type{4}) = GAbinary(Parent1(:,Type{4}),Parent2(:,Type{4}),proC,proM*length(Type{4})/size(Parent1,2)); + end + if ~isempty(Type{5}) % Permutation variables + Offspring(:,Type{5}) = GApermutation(Parent1(:,Type{5}),Parent2(:,Type{5}),proC); + end + if evaluated + Offspring = Problem.Evaluation(Offspring); + end +end + +function Offspring = GAreal(Parent1,Parent2,lower,upper,proC,disC,proM,disM, Non0_index) +% Genetic operators for real and integer variables + + [N,D] = size(Parent1); + %% Genetic operators for real encoding + % Simulated binary crossover + beta = zeros(N,D); + mu = rand(N,D); + beta(mu<=0.5) = (2*mu(mu<=0.5)).^(1/(disC+1)); + beta(mu>0.5) = (2-2*mu(mu>0.5)).^(-1/(disC+1)); + beta = beta.*(-1).^randi([0,1],N,D); % random generate 1 或 -1 + beta(rand(N,D)<0.5) = 1; + beta(repmat(rand(N,1)>proC,1,D)) = 1; + Offspring = [(Parent1+Parent2)/2+beta.*(Parent1-Parent2)/2 + (Parent1+Parent2)/2-beta.*(Parent1-Parent2)/2]; + % Polynomial mutation + % only mutate in non-zero variable + Lower = repmat(lower,2*N,1); + Upper = repmat(upper,2*N,1); + Offspring = min(max(Offspring,Lower),Upper); + + % set the probability of mutation and variable to be mutated + Site = rand(2*N,D) < proM/sum(Non0_index); + mu = rand(2*N,D); + + temp = Non0_index & Site & mu<=0.5; + Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*((2.*mu(temp)+(1-2.*mu(temp)).*... + (1-(Offspring(temp)-Lower(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))-1); + % mu>0.5 + temp = Non0_index & Site & mu>0.5; + Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*(1-(2.*(1-mu(temp))+2.*(mu(temp)-0.5).*... + (1-(Upper(temp)-Offspring(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))); +end + +function Offspring = GAlabel(Parent1,Parent2,lower,upper,proC,proM) +% Genetic operators for label variables + + %% Uniform crossover + [N,D] = size(Parent1); + k = rand(N,D) < 0.5; + k(repmat(rand(N,1)>proC,1,D)) = false; + Offspring1 = Parent1; + Offspring2 = Parent2; + Offspring1(k) = Parent2(k); + Offspring2(k) = Parent1(k); + Offspring = [Offspring1;Offspring2]; + + %% Bitwise mutation + Site = rand(2*N,D) < proM/D; + Rand = round(unifrnd(repmat(lower,2*N,1),repmat(upper,2*N,1))); + Offspring(Site) = Rand(Site); +end + +function Offspring = GAbinary(Parent1,Parent2,proC,proM) +% Genetic operators for binary variables + + %% Uniform crossover + [N,D] = size(Parent1); + k = rand(N,D) < 0.5; + k(repmat(rand(N,1)>proC,1,D)) = false; + Offspring1 = Parent1; + Offspring2 = Parent2; + Offspring1(k) = Parent2(k); + Offspring2(k) = Parent1(k); + Offspring = [Offspring1;Offspring2]; + + %% Bit-flip mutation + Site = rand(2*N,D) < proM/D; + Offspring(Site) = ~Offspring(Site); +end + +function Offspring = GApermutation(Parent1,Parent2,proC) +% Genetic operators for permutation variables + + %% Order crossover + [N,D] = size(Parent1); + Offspring = [Parent1;Parent2]; + k = randi(D,1,2*N); + for i = 1 : N + if rand < proC + Offspring(i,k(i)+1:end) = setdiff(Parent2(i,:),Parent1(i,1:k(i)),'stable'); + Offspring(i+N,k(i)+1:end) = setdiff(Parent1(i,:),Parent2(i,1:k(i)),'stable'); + end + end + + %% Slight mutation + k = randi(D,1,2*N); + s = randi(D,1,2*N); + for i = 1 : 2*N + if s(i) < k(i) + Offspring(i,:) = Offspring(i,[1:s(i)-1,k(i),s(i):k(i)-1,k(i)+1:end]); + elseif s(i) > k(i) + Offspring(i,:) = Offspring(i,[1:k(i)-1,k(i)+1:s(i)-1,k(i),s(i):end]); + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/MOEA-NZD/MOEANZD.m b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-NZD/MOEANZD.m new file mode 100644 index 000000000..5f31fb2f9 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-NZD/MOEANZD.m @@ -0,0 +1,71 @@ +classdef MOEANZD < ALGORITHM +% +% Multi-objective evolutionary algorithm with nonzero detection + +%------------------------------- Reference -------------------------------- +% X. Wang, R. Cheng, and Y. Jin, Sparse large-scale multiobjective +% optimization by identifying nonzero decision variables, IEEE Transactions +% on Systems, Man, and Cybernetics: Systems, 2024. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Xiangyu Wang (email: xiangyu.wang@uni-bielefeld.de) + + methods + function main(Algorithm,Problem) + %% Generate the reference points and random population + [Z,Problem.N] = UniformPoint(Problem.N,Problem.M); + Mask = zeros(Problem.N, Problem.D); % Generate population with all zeros. + Population = Problem.Evaluation(Mask); + Zmin = min(Population(all(Population.cons<=0,2)).objs,[],1); + + %% Optimization + % predefined parameter + Re0 = false(1, Problem.D); + Re1 = true(1, Problem.D); + t = 1; + t1 = 1; + evaluation = 0; + Demarcation = floor(Problem.maxFE * 0.7 / Problem.N); + intervel = floor(Problem.maxFE / Problem.N / 20); + while Algorithm.NotTerminated(Population) + evaluation = evaluation + 1; + MatingPool = TournamentSelection(2,Problem.N,sum(max(0,Population.cons),2)); + NextStep = mean(abs(Re1-Re0)) >= (1/Problem.D); + if evaluation < Demarcation + if NextStep && mod(evaluation,intervel) == 0 + if t == 1 + t = t + 1; + else + Re0 = Re1; + t = t + 1; + end + [Re1, PopulationM] = DimJud(Population(MatingPool).decs, Problem.upper, Problem.lower, Re0); + else + PopulationM = Population(MatingPool).decs; + end + Offspring = OperatorGA(Problem, PopulationM, {1,20,1,1}); + Offspring = Problem.Evaluation(Offspring); + else + Offspring0 = Population(MatingPool).decs; + if t1 == 1 || mod(evaluation,intervel) == 0 + [Offspring,Non0_index] = DimJud0(Offspring0, Problem); + t1 = t1 + 1; + else + Offspring = GA0(Offspring0,Non0_index, Problem); + end + Offspring = Problem.Evaluation(Offspring); + Offspring0 = []; + end + Zmin = min([Zmin;Offspring(all(Offspring.cons<=0,2)).objs],[],1); + Population = EnvironmentalSelection([Population,Offspring],Problem.N,Z,Zmin); + end + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/NSBiDiCo/EnvironmentalSelection.m b/PlatEMO/Algorithms/Multi-objective optimization/NSBiDiCo/EnvironmentalSelection.m new file mode 100644 index 000000000..658fedfea --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/NSBiDiCo/EnvironmentalSelection.m @@ -0,0 +1,31 @@ +function [Population,FrontNo,CrowdDis] = EnvironmentalSelection(Population,N) +% The environmental selection of NSBiDiCo + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Lucas Farias (email: lrcf@cin.ufpe.br) + + %% Non-dominated sorting + [FrontNo,MaxFNo] = NDSort(Population.objs,Population.cons,N); + Next = FrontNo < MaxFNo; + + %% Calculate the crowding distance of each solution + CrowdDis = CrowdingDistance(Population.objs,FrontNo); + + %% Select the solutions in the last front based on their crowding distances + Last = find(FrontNo==MaxFNo); + [~,Rank] = sort(CrowdDis(Last),'descend'); + Next(Last(Rank(1:N-sum(Next)))) = true; + + %% Population for next generation + Population = Population(Next); + FrontNo = FrontNo(Next); + CrowdDis = CrowdDis(Next); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/NSBiDiCo/MatingSelection.m b/PlatEMO/Algorithms/Multi-objective optimization/NSBiDiCo/MatingSelection.m new file mode 100644 index 000000000..26ab0727c --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/NSBiDiCo/MatingSelection.m @@ -0,0 +1,52 @@ +function MatingPool = MatingSelection(Population,ArcPop,N) + +%-------------------------------------------------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB Platform +% for Evolutionary Multi-Objective Optimization [Educational Forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Lucas Farias (email: lrcf@cin.ufpe.br) + + MatingPool = []; + if length(ArcPop) < N + SelectedIndex = TournamentSelection(2,N,-sum(max(0,Population.cons),2)); + MatingPool = Population(SelectedIndex); + else + AllPop = [ Population,ArcPop]; + Zmin = min(AllPop.objs,[],1); + PopObj = (AllPop.objs-repmat(Zmin,length(AllPop.objs),1))./(repmat(max(AllPop.objs),length(AllPop.objs),1)-repmat(Zmin,length(AllPop.objs),1)+1e-10)+1e-10; + Cosine = 1 - pdist2(PopObj,PopObj,'cosine'); + Cosine = Cosine.*(1-eye(size(PopObj,1))); + + Temp = sort(-Cosine,2); + [~,Rank] = sortrows(Temp); + + CV1 = sum(max(0,Population.cons),2); + CV2 = sum(max(0,ArcPop.cons),2); + + Angle1 = Rank(1:N); + Angle2 = Rank(N+1:length(AllPop)); + + Index1 = randi(N,1,N); + Index2 = randi(length(ArcPop),1,N); + + i = 0; + while length(MatingPool) < N + if CV1(Index1(i+1)) < CV2(Index2(i+1)) + MatingPool = [MatingPool,Population(Index1(i+1))]; + else + MatingPool = [MatingPool,ArcPop(Index2(i+1))]; + end + if Angle1(Index1(i+2)) < Angle2(Index2(i+2)) + MatingPool = [MatingPool,Population(Index1(i+2))]; + else + MatingPool = [MatingPool,ArcPop(Index2(i+2))]; + end + i = i + 2; + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/NSBiDiCo/NSBiDiCo.m b/PlatEMO/Algorithms/Multi-objective optimization/NSBiDiCo/NSBiDiCo.m new file mode 100644 index 000000000..44790780c --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/NSBiDiCo/NSBiDiCo.m @@ -0,0 +1,39 @@ +classdef NSBiDiCo < ALGORITHM +% +% Non-dominated sorting bidirectional differential coevolution algorithm + +%------------------------------- Reference -------------------------------- +% C. S. R. Mendes, A. F. R. Araujo, and L. R. C. Farias. Non-dominated +% sorting bidirectional differential coevolution. Proceedings of the IEEE +% International Conference on Systems, Mans and Cybernetics, 2023. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB Platform +% for Evolutionary Multi-Objective Optimization [Educational Forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Lucas Farias (email: lrcf@cin.ufpe.br) + + methods + function main(Algorithm,Problem) + %% Parameter setting + [Cr,F,proM,disM] = Algorithm.ParameterSet(1,0.5,1,20); + + %% Generate random population + Population = Problem.Initialization(); + ArcPop = []; + + %% Optimization + while Algorithm.NotTerminated(Population) + AllPop = [Population,ArcPop]; + MatingPool = MatingSelection(Population,ArcPop,Problem.N); + Offspring = OperatorDE(Problem, MatingPool(1:end), MatingPool(randi(Problem.N,1,Problem.N)), MatingPool(randi(Problem.N,1,Problem.N)), {Cr, F, proM, disM}); + ArcPop = UpdateArc([AllPop, Offspring], Problem.N); + Population = EnvironmentalSelection([Population, Offspring], Problem.N); + end + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/NSBiDiCo/UpdateArc.m b/PlatEMO/Algorithms/Multi-objective optimization/NSBiDiCo/UpdateArc.m new file mode 100644 index 000000000..01b0499b6 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/NSBiDiCo/UpdateArc.m @@ -0,0 +1,68 @@ +function ArcPop = UpdateArc(Population,N) + +%-------------------------------------------------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB Platform +% for Evolutionary Multi-Objective Optimization [Educational Forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Lucas Farias (email: lrcf@cin.ufpe.br) + + FrontNo = NDSort([Population.objs,sum(max(0,Population.cons),2)],1); + Temp1 = FrontNo==1; + + Population = Population(Temp1==1); + + Temp = find(sum(max(0,Population.cons),2)>0); + Population = Population(Temp); + + if length(Population)= 1 + Offspring = Problem.Evaluation(OffspringDec(in,:)); + Samples = Offspring; + Population(~out) = Offspring; + else + Samples = Population; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/SSDE/SSDE.m b/PlatEMO/Algorithms/Multi-objective optimization/SSDE/SSDE.m new file mode 100644 index 000000000..3286b370b --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/SSDE/SSDE.m @@ -0,0 +1,61 @@ +classdef SSDE < ALGORITHM +% +% Self-organized surrogate-assisted differential evolution +% num_nodes --- --- Number of neurons in each dimension of the latent space +% eta0 --- 0.2 --- Initial learning rate +% sigma0 --- --- Size of neighborhood mating pools + +%------------------------------- Reference -------------------------------- +% A. F. R. Araújo, L. R. C. Farias, and A. R. C. Gonçalves, Self-organizing +% surrogate-assisted non-dominated sorting differential evolution, Swarm +% and Evolutionary Computation, 2024. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Lucas Farias (email: lrcf@cin.ufpe.br) + + methods + function main(Algorithm,Problem) + %% Parameter setting + [num_nodes,eta0,sigma0] = Algorithm.ParameterSet(Problem.N, 0.2, Problem.N); + + %% Generate random population + Population = Problem.Initialization(); + + %% Initialize the sample set + Samples = Population; + + %% Initialize the SOM weight vectors + a = 0.001; + b = 0.5; + W = a.*randn(num_nodes,Problem.D+Problem.M) + b; + winning_weights = false(1,Problem.N); + + %% Position of each neuron and neighborhood + D = arrayfun(@(S)1:S,floor(num_nodes),'UniformOutput',false); + eval(sprintf('[%s]=ndgrid(D{:});',sprintf('c%d,',1:length(D)))) + eval(sprintf('V=[%s];',sprintf('c%d(:),',1:length(D)))) + LDis = pdist2(V,V); % Distance between each two neurons in latent space + + %% Optimization + while Algorithm.NotTerminated(Population) + %% Training + if size(Samples,2) >= Problem.N + W = Training(Problem, W, LDis, Samples, num_nodes, eta0, sigma0, winning_weights); + winning_weights = false(1,num_nodes); + Samples = []; + end + + %% Evolutionary process + [Population, new_samples, winning_weights] = Operator(Problem, Population, W, winning_weights); + Samples = [Samples, new_samples]; + end + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/SSDE/Training.m b/PlatEMO/Algorithms/Multi-objective optimization/SSDE/Training.m new file mode 100644 index 000000000..7e7e105ed --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/SSDE/Training.m @@ -0,0 +1,74 @@ +function W = Training(Problem, W, LDis, Samples, num_nodes, eta0, sigma0, winning_weights) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Lucas Farias (email: lrcf@cin.ufpe.br) + + %% Memory-based reinitialization + if any(winning_weights) == true + %% If there is a node that did not win + if sum(winning_weights) < num_nodes + %% Select indexes based on diversity criteria + [FrontNo,~] = NDSort(W(winning_weights,Problem.D+1:end),sum(winning_weights)); + + %% Calculate the crowding distance of each solution + CrowdDis = CrowdingDistance(W(winning_weights,Problem.D+1:end),FrontNo); + [~,p] = sort(CrowdDis); + factor = 1 : length(CrowdDis); + factor(p) = factor; % each winning node receives a weight according to the diversity criterion + chosen_idx = randsample(sum(winning_weights),2*(num_nodes-sum(winning_weights)),true,factor); + chosen_idx = reshape(chosen_idx,num_nodes-sum(winning_weights),2); + + % linear combination: calculate average and add noise to new nodes + W(~winning_weights,:) = ((W(chosen_idx(:,1),:) + W(chosen_idx(:,2),:) ) ./ 2) + 0.001.*randn(num_nodes-sum(winning_weights),Problem.D+Problem.M); + + % repair if it exceeds the upper limit, 1, or lower limit, 0 + W(:,1:Problem.D) = max(min(W(:,1:Problem.D),1),0); + end + end + + %% Normalize Sample Set + Samples = [Samples.decs,Samples.objs]; + Samples(:,1:Problem.D) = rescale(Samples(:,1:Problem.D),'InputMin',Problem.lower,'InputMax',Problem.upper); + + % reset parameters + win_count_set = zeros(1,num_nodes); + + % SOM surrogate model training + for epoch = 1 : 50 + % shuffle the samples + randpos = randperm(size(Samples,1)); + % Neighborhood radius + sigma = sigma0*exp((-win_count_set/size(Samples,1))); + % Learning rate + eta = eta0*exp((-win_count_set/size(Samples,1))); + + for i = 1 : size(Samples,1) + s = randpos(i); + + % define winning node, u1, from input sample, s, using quadratic Euclidean distance + [~,u1] = min(pdist2(Samples(s,1:end-Problem.M),W(:,1:end-Problem.M))); + + % If it is the node's first win, assign the sample to the node + if win_count_set(u1) == 0 + W(u1,:) = Samples(s,:); + end + + % limit node win counter to current epoch + if win_count_set(u1) < epoch + win_count_set(u1) = win_count_set(u1) + 1; + end + + % update winning node and its neighborhood + U = LDis(u1,:) < sigma; + W(U,:) = W(U,:) + repmat(eta(U)',1,size(W,2)).*repmat(exp(-LDis(u1,U))',1,size(W,2)).*(repmat(Samples(s,:),sum(U),1)-W(U,:)); + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/TELSO/EnvironmentalSelection.m b/PlatEMO/Algorithms/Multi-objective optimization/TELSO/EnvironmentalSelection.m new file mode 100644 index 000000000..5faf057d3 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/TELSO/EnvironmentalSelection.m @@ -0,0 +1,59 @@ +function [Population,Mask2] = EnvironmentalSelection(Population,V,theta,Mask) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Sheng Qi (email: 2745679162@qq.com) + + Mask = repmat(Mask, 2, 1); + PopObj = Population.objs; + [N,M] = size(PopObj); + NV = size(V,1); + + %% Translate the population + PopObj = PopObj - repmat(min(PopObj,[],1),N,1); + + %% Calculate the degree of violation of each solution + CV = sum(max(0,Population.cons),2); + + %% Calculate the smallest angle value between each vector and others + cosine = 1 - pdist2(V,V,'cosine'); + cosine(logical(eye(length(cosine)))) = 0; + gamma = min(acos(cosine),[],2); + + %% Associate each solution to a reference vector + Angle = acos(1-pdist2(PopObj,V,'cosine')); + [~,associate] = min(Angle,[],2); + + %% Select one solution for each reference vector + Next = zeros(1,NV); + for i = unique(associate)' + current1 = find(associate==i & CV==0); + current2 = find(associate==i & CV~=0); + if ~isempty(current1) + % Calculate the APD value of each solution + APD = (1+M*theta*Angle(current1,i)/gamma(i)).*sqrt(sum(PopObj(current1,:).^2,2)); + % Select the one with the minimum APD value + [~,best] = min(APD); + Next(i) = current1(best); + elseif ~isempty(current2) + % Select the one with the minimum CV value + [~,best] = min(CV(current2)); + Next(i) = current2(best); + end + end + % Population for next generation + Population = Population(Next(Next~=0)); + j = length(Next(Next~=0)); + a = Next(Next~=0); + for i = 1 : j + b = a(i); + Mask2(i, :) = Mask(b, :); + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/TELSO/Initial_EnvironmentalSelection.m b/PlatEMO/Algorithms/Multi-objective optimization/TELSO/Initial_EnvironmentalSelection.m new file mode 100644 index 000000000..d6990888e --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/TELSO/Initial_EnvironmentalSelection.m @@ -0,0 +1,32 @@ +function [Population,Mask] = Initial_EnvironmentalSelection(Population,Dec,Mask,N) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Sheng Qi (email: 2745679162@qq.com) + + %% Non-dominated sorting + [FrontNo,MaxFNo] = NDSort(Population.objs,Population.cons,N); + Next = FrontNo < MaxFNo; + + %% Calculate the crowding distance of each solution + CrowdDis = CrowdingDistance(Population.objs,FrontNo); + + %% Select the solutions in the last front based on their crowding distances + Last = find(FrontNo==MaxFNo); + [~,Rank] = sort(CrowdDis(Last),'descend'); + Next(Last(Rank(1:N-sum(Next)))) = true; + + %% Population for next generation + Population = Population(Next); + FrontNo = FrontNo(Next); + CrowdDis = CrowdDis(Next); + Dec = Dec(Next,:); + Mask = Mask(Next,:); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/TELSO/Operator.m b/PlatEMO/Algorithms/Multi-objective optimization/TELSO/Operator.m new file mode 100644 index 000000000..069ab1e3e --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/TELSO/Operator.m @@ -0,0 +1,56 @@ +function [Offspring,Mask] = Operator(Population,Mask,Problem) +% The learning swarm optimizer of TELSO + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Sheng Qi (email: 2745679162@qq.com) + + %% Parameter setting + PopulationDec = Population.decs; + [N,D] = size(PopulationDec); + PopulationVel = Population.adds(zeros(N,D)); + OffDec = []; + OffVel = []; + + %% Refactoring the real variable + for i = 1 : (N-2) + rand_indices = randi([i+1,N],1,2); + selected_particles = Population(rand_indices); + r1 = repmat(rand(1,1),1,D); + r2 = repmat(rand(1,1),1,D); + r3 = repmat(rand(1,1),1,D); + p_OffVel = r1.*PopulationVel(i)+r2.*(selected_particles(1).dec-Population(i).dec)+r3.*(selected_particles(2).dec-Population(i).dec); + p_OffDec = Population(i).dec+p_OffVel; + OffDec = [OffDec;p_OffDec]; + OffVel = [OffVel;p_OffVel]; + + p1_mask = Mask(rand_indices(1), :); + p2_mask = Mask(rand_indices(2), :); + if rand < 0.5 + same_elems = (p1_mask == p2_mask & p2_mask ==1); + else + same_elems = (p1_mask == p2_mask & p2_mask ==0); + end + same_cols = find(same_elems); + D_number = numel(same_cols); + keep = floor(D_number * Problem.FE/Problem.maxFE); + same_cols = same_cols(1:keep); + if same_cols ~= 0 + for j = same_cols + Mask(i,j) = Mask(rand_indices(1),j); + end + end + end + + %% Add the two winners + OffDec = [OffDec;Population(N-1).dec;Population(N).dec]; + OffVel = [OffVel;Population(N-1).add;Population(N).add]; + Offspring = Problem.Evaluation(OffDec.*Mask,OffVel); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/TELSO/TELSO.m b/PlatEMO/Algorithms/Multi-objective optimization/TELSO/TELSO.m new file mode 100644 index 000000000..8184fe125 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/TELSO/TELSO.m @@ -0,0 +1,88 @@ +classdef TELSO < ALGORITHM +% +% Two-layer encoding learning swarm optimizer + +%------------------------------- Reference -------------------------------- +% Qi S, Wang R, Zhang T, X. Yang, R. Sun, and L. Wang. A two-layer encoding +% learning swarm optimizer based on frequent itemsets for sparse +% large-scale multi-objective optimization. IEEE/CAA Journal of Automatica +% Sinica, 2024, 11(6): 1342-1357. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Sheng Qi (email: 2745679162@qq.com) + + methods + function main(Algorithm,Problem) + %% Initialization reference vector + [V,Problem.N] = UniformPoint(Problem.N,Problem.M); + + %% Generate random population + % Calculate the fitness of each decision variable + TDec = []; + TMask = []; + TempPop = []; + DF = zeros(1,Problem.D); % The fitness of decision variables + REAL = all(Problem.encoding==1); + for i = 1 : 1+4*REAL + if REAL + Dec = unifrnd(repmat(Problem.lower,Problem.D,1),repmat(Problem.upper,Problem.D,1)); + else + Dec = ones(Problem.D,Problem.D); + end + Mask = eye(Problem.D); + Population = Problem.Evaluation(Dec.*Mask); + TDec = [TDec;Dec]; + TMask = [TMask;Mask]; + TempPop = [TempPop,Population]; + DF = DF + NDSort([Population.objs,Population.cons],inf); + end + + %% Generate initial population + if REAL + Dec = unifrnd(repmat(Problem.lower,Problem.N,1),repmat(Problem.upper,Problem.N,1)); + else + Dec = ones(Problem.N,Problem.D); + end + Mask = zeros(Problem.N,Problem.D); + for i = 1 : Problem.N + Mask(i,TournamentSelection(2,ceil(rand*Problem.D),DF)) = 1; + end + Population = Problem.Evaluation(Dec.*Mask); + [Population,Mask] = Initial_EnvironmentalSelection([Population,TempPop],[Dec;TDec],[Mask;TMask],Problem.N); + [Population,Mask] = EnvironmentalSelection(Population,V,(Problem.FE/Problem.maxFE)^2,Mask); + + %% Optimization + while Algorithm.NotTerminated(Population) + Fitness = CalFitness(Population.objs); + [~,index] = sort(Fitness); + Population = Population(index); + Mask = Mask(index,:); + [Offspring,Mask] = Operator(Population,Mask,Problem); + [Population,Mask] = EnvironmentalSelection([Population,Offspring],V,(Problem.FE/Problem.maxFE)^2,Mask); + end + end + end +end + +function Fitness = CalFitness(PopObj) +% Calculate the fitness by shift-based density + N = size(PopObj,1); + fmax = max(PopObj,[],1); + fmin = min(PopObj,[],1); + PopObj = (PopObj-repmat(fmin,N,1))./repmat(fmax-fmin,N,1); + Dis = inf(N); + for i = 1 : N + SPopObj = max(PopObj,repmat(PopObj(i,:),N,1)); + for j = [1:i-1,i+1:N] + Dis(i,j) = norm(PopObj(i,:)-SPopObj(j,:)); + end + end + Fitness = min(Dis,[],2); +end \ No newline at end of file diff --git a/PlatEMO/GUI/Draw.m b/PlatEMO/GUI/Draw.m index df64c8768..d86020820 100644 --- a/PlatEMO/GUI/Draw.m +++ b/PlatEMO/GUI/Draw.m @@ -40,7 +40,7 @@ if size(Data,2) == 1 Data = [(1:size(Data,1))',Data]; end - set(ax,'FontName','Times New Roman','FontSize',13,'NextPlot','add','Box','on','View',[0 90],'GridLineStyle','none'); + set(ax,'FontName','Times New Roman','FontSize',13,'NextPlot','add','Box','on','View',[0 90],'YScale','linear'); if islogical(Data) [ax.XLabel.String,ax.YLabel.String,ax.ZLabel.String] = deal('Solution No.','Dimension No.',[]); elseif size(Data,2) > 3 @@ -61,6 +61,8 @@ elseif size(Data,2) > 3 varargin = {'-','Color',[.5 .5 .5],'LineWidth',2}; end + elseif min(Data(:,2)) > 0 && max(Data(:,2)) > 10*min(Data(:,2)) + set(ax,'YScale','log'); end if islogical(Data) C = zeros(size(Data)) + 0.6; diff --git a/PlatEMO/GUI/GUI.m b/PlatEMO/GUI/GUI.m index 2a0fdc820..9945961e8 100644 --- a/PlatEMO/GUI/GUI.m +++ b/PlatEMO/GUI/GUI.m @@ -25,7 +25,7 @@ obj.readList(); % Create the window - obj.app.figure = uifigure('Name','PlatEMO v4.7','Position',[0 0 1200 650],'Interruptible','off','icon',obj.icon.logo1,'BusyAction','cancel','Visible','off','WindowButtonMotionFcn',@(~,~)[]); + obj.app.figure = uifigure('Name','PlatEMO v4.8','Position',[0 0 1200 650],'Interruptible','off','icon',obj.icon.logo1,'BusyAction','cancel','Visible','off','WindowButtonMotionFcn',@(~,~)[]); obj.app.maingrid = uigridlayout(obj.app.figure,'RowHeight',{25,80,'1x'},'ColumnWidth',{'1x'},'Padding',[0 0 0 0],'RowSpacing',0); % Create the tab buttons @@ -122,7 +122,7 @@ function readList(obj) function List = readList2(obj,folder,LabelStr) List = {}; Folders = split(genpath(fullfile(fileparts(mfilename('fullpath')),'..',folder)),pathsep); - for i = 1 : length(Folders) + for i = 1 : length(Folders) - 1 Files = what(Folders{i}); Files = Files.m; for j = 1 : length(Files) @@ -162,7 +162,7 @@ function cb_module(obj,~,~,GUIsetting) function cb_author(obj,~,~) P = obj.app.figure.Position; C = obj.app.figure.CurrentPoint; - f = uifigure('Name','About PlatEMO','Position',[P(1)+C(1) P(2)+C(2)-220 290 200],'Color','w','icon',obj.icon.logo1,'Resize','off'); + f = uifigure('Name','About PlatEMO','Position',[P(1)+C(1) P(2)+C(2)-220 290 200],'Color','w','icon',obj.icon.logo1,'WindowStyle','modal','Resize','off'); uiimage(f,'Position',[145 40 150 150],'ImageSource',obj.icon.logo2); uilabel(f,'Position',[10 160 170 30],'Text',obj.app.figure.Name,'HorizontalAlignment','left','FontSize',18); uilabel(f,'Position',[10 130 160 40],'Text',{'Evolutionary Multi-Objective','Optimization Platform'},'HorizontalAlignment','left','FontSize',12,'FontAngle','italic','FontColor',[.4 .4 .4]); @@ -193,5 +193,117 @@ function delete(obj) app.Layout.Row = row; app.Layout.Column = column; end + %% Generate state buttons of labels + function [stateButton,label] = GenerateLabelButton(grid,values,cbFcn) + label(1) = GUI.APP(1,[1 3],uilabel(grid,'Text','Number of objectives','VerticalAlignment','bottom','FontSize',12,'FontColor',[.15 .6 .2],'FontWeight','bold')); + stateButton(1) = GUI.APP(2,1,uibutton(grid,'state','Text','single','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',values(1),'Tooltip','The problem has a single objective','ValueChangedFcn',{cbFcn,1})); + stateButton(2) = GUI.APP(2,2,uibutton(grid,'state','Text','multi','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',values(2),'Tooltip','The problem has 2 or 3 objectives','ValueChangedFcn',{cbFcn,2})); + stateButton(3) = GUI.APP(2,3,uibutton(grid,'state','Text','many','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',values(3),'Tooltip','The problem has more than 3 objectives','ValueChangedFcn',{cbFcn,3})); + label(2) = GUI.APP(3,[1 3],uilabel(grid,'Text','Encoding scheme','VerticalAlignment','bottom','FontSize',12,'FontColor',[.15 .6 .2],'FontWeight','bold')); + stateButton(4) = GUI.APP(4,1,uibutton(grid,'state','Text','real','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',values(4),'Tooltip','The decision variables are real numbers','ValueChangedFcn',{cbFcn,4})); + stateButton(5) = GUI.APP(4,2,uibutton(grid,'state','Text','integer','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',values(5),'Tooltip','The decision variables are integers','ValueChangedFcn',{cbFcn,5})); + stateButton(6) = GUI.APP(4,3,uibutton(grid,'state','Text','label','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',values(6),'Tooltip','The decision variables are labels','ValueChangedFcn',{cbFcn,6})); + stateButton(7) = GUI.APP(5,1,uibutton(grid,'state','Text','binary','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',values(7),'Tooltip','The decision variables are binary numbers','ValueChangedFcn',{cbFcn,7})); + stateButton(8) = GUI.APP(5,2,uibutton(grid,'state','Text','permutation','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',values(8),'Tooltip','The decision vector is a permutation','ValueChangedFcn',{cbFcn,8})); + label(3) = GUI.APP(6,[1 3],uilabel(grid,'Text','Special difficulties','VerticalAlignment','bottom','FontSize',12,'FontColor',[.15 .6 .2],'FontWeight','bold')); + stateButton(9) = GUI.APP(7,1,uibutton(grid,'state','Text','large','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',values(9),'Tooltip','The problem has more than 100 decision variables','ValueChangedFcn',{cbFcn,9})); + stateButton(10) = GUI.APP(7,2,uibutton(grid,'state','Text','constrained','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',values(10),'Tooltip','The problem has constraints','ValueChangedFcn',{cbFcn,10})); + stateButton(11) = GUI.APP(7,3,uibutton(grid,'state','Text','expensive','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',values(11),'Tooltip','The objectives are computationally time-consuming','ValueChangedFcn',{cbFcn,11})); + stateButton(12) = GUI.APP(8,1,uibutton(grid,'state','Text','multimodal','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',values(12),'Tooltip','The objectives are multimodal','ValueChangedFcn',{cbFcn,12})); + stateButton(13) = GUI.APP(8,2,uibutton(grid,'state','Text','sparse','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',values(13),'Tooltip','Most decision variables of the optimal solutions are zero','ValueChangedFcn',{cbFcn,13})); + stateButton(14) = GUI.APP(8,3,uibutton(grid,'state','Text','dynamic','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',values(14),'Tooltip','The objectives vary periodically','ValueChangedFcn',{cbFcn,14})); + stateButton(15) = GUI.APP(9,1,uibutton(grid,'state','Text','multitask','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',values(15),'Tooltip','The problem has multiple tasks to be solved simultaneously','ValueChangedFcn',{cbFcn,15})); + stateButton(16) = GUI.APP(9,2,uibutton(grid,'state','Text','bilevel','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',values(16),'Tooltip','The problem has two nested objectives','ValueChangedFcn',{cbFcn,16})); + stateButton(17) = GUI.APP(9,3,uibutton(grid,'state','Text','robust','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',values(17),'Tooltip','The objectives are influenced by uncertain factors','ValueChangedFcn',{cbFcn,17})); + end + %% Update the list of algorithms and problems + function func = UpdateAlgProList(index,stateButton,varargin) + if index > 0 + if index < 4 + [stateButton(1:3).Value] = deal(0); + stateButton(index).Value = 1; + elseif index < 9 + [stateButton(4:8).Value] = deal(0); + stateButton(index).Value = 1; + end + end + filter = [stateButton.Value]; + func = @(s)all(any(repmat([true,filter],size(s,1),1)&s,2)) && all((any(s(:,2:end),1)&filter)==filter); + for i = 1 : 3 : length(varargin) + [listBox,label,list] = deal(varargin{i:i+2}); + show = cellfun(func,list(:,1)); + listBox.Items = ['(Open File)';list(show,2)]; + listBox.Value = {}; + label.Text = sprintf('%d / %d',sum(show),length(show)); + end + end + %% Update the parameter list of algorithms and problems + function UpdateAlgProPara(fig,list,paraList,fileType,paraType) + filename = list.Value; + if contains(filename,'Open File') + [Name,Path] = uigetfile({'*.m','MATLAB class'}); + figure(fig); + if Name ~= 0 + try + filename = fullfile(Path,Name); + f = fopen(filename); + str = fgetl(f); + fclose(f); + assert(contains(str,['< ',fileType])); + addpath(Path); + catch + uialert(fig,['The selected file is not a subclass of ',fileType,'.'],'Error'); + return; + end + else + return; + end + else + filename = [filename,'.m']; + end + if abs(paraType) ~= 2 + paraList.del([],paraType); + end + paraList.add(filename,paraType); + paraList.flush(); + end + %% Read parameter settings from the parameter list + function [name,para] = GetParameterSetting(listItem) + name = listItem.title.Text; + para = cell(1,length(listItem.edit)); + for j = 1 : length(para) + if ~isempty(listItem.edit(j).Value) + para{j} = str2num(listItem.edit(j).Value); + assert(~isempty(para{j}),'the parameter "%s" of %s is illegal.',listItem.label(j).Text,listItem.title.Text); + end + end + end + %% Save a population + function SavePopulation(fig,Population,type) + if type == 1 + Population = Population.best; + end + if isempty(Population) + uialert(fig,'No solution can be saved, since all solutions are infeasible.','Error'); + else + Data = [Population.decs,Population.objs,Population.cons]; + try + [Name,Path] = uiputfile({'*.txt','Text file';'*.dat','Text file';'*.csv','Text file';'*.mat','MAT file';'*.xlsx','Excel table'},'','data'); + figure(fig); + if ischar(Name) + [~,~,Type] = fileparts(Name); + switch Type + case '.mat' + save(fullfile(Path,Name),'Data','-mat'); + otherwise + writematrix(Data,fullfile(Path,Name)); + end + end + catch err + uialert(fig,'Fail to save the result, please refer to the command window for details.','Error'); + rethrow(err); + end + end + end end end \ No newline at end of file diff --git a/PlatEMO/GUI/GUI.mat b/PlatEMO/GUI/GUI.mat index 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z1x9nMos$s1J~IRC$q7mK`|5BYx@EN*9O5#6rTa$gO)A+6PN}0ir_{5IPhYLo-U3&8 z*Cp?}(*Ns*mTn4z5u2ao+NZ7_SG&utW>)_7tM)j)CpLjoIv9@jELM+=xRQKC!CmfZ za7llcaj5rpe;YqawG6A7bXrVn&eb^%2Q%PN*G-LHP2S^tu$dCufTgKFO9u&`YQTpT ze6WUVYq#0^di>;pm}=3o9&SFFY`#Wvv# zoZ4BD#dN3@KCIwwUF%Bkj&8@@bx~{ur|W-VRjf`fd$hYsyX9LA;!^E?86Tzotcq(F&ReV9BjZlZ#`W9hmQ(2#aHa{YnOrWHe)(*9Y2jfId;EX% z59+oDCY9QKI$W0=@-BP-ovn2Ggut0P5ezuw3%TURlECnfe|x(4@b>pV)@oOh`_zS# zUZ@@oF55#7_OflmInCsYnb+$p9Dxz!qk9Y3Pv6ZF>O&?FsNr7eAtU21eeHez;9>6g z%d1E8>J+oP&pDQ-aSW$eoO<;8^-F(VIr(&;ZsFu79K6k|$zkiZscjp=8YCONIn3buJE;@B=5J+u4JIJ)3zqxKJpRK;bYohEW{J3Q?9@+PeMTX%v6$_@H{t zwaL9QJ}Bezrn&lEcXo2wrf~DX1!>B;{bv-00~|C?vkXJhPlLDKH+5P%8j|}Pak+Mf z=l)*4t(FV5DV7Tds}V97ggAjO(jl54sh&$;oVIpa5Cg!kX_D9D0}AeR3_W?9e;2q2 zrc^pykMkTw;5fbrKiDu$P~m^iJ~W#Qod%N$;xih2tPzK`+eXM_{&={chXGv{*AfEH z@eDy=!5~Ru7%7k#^X#UfimD^NT#AOkL?QymfYdlak_18X!3CDGeS#QG zM&mh)QoDBHM(eiSKJz?(!sXS&rE)&Gy&Q{%Ljuq99LLcNPEZsTTx2+sON^bpU_mSh z`jMq>+u?mK>C3kjq60ukW>O8};3{6uMO;Tj|AAz6$*kCc6 zfo?U|FI-)6kHL7nPc)-n|2x7LHOUuMfY^uodpnzJE3tSq5()_dc%HH}h)vR9Zvk`H-Y^IN5YQulFirCj+B(3Mga{9b)~u)VPsizT9AuyHJ0 zH-@1=>3EP1a{*H`f^=F@5WUL+YX@Imr=?%1^~r}^UvC0i({roDl3b}2Q*VxvZx8lg zZEr=R@d%90G8_Z0IlRw;7$s1bjaKV9Gh#w4md>vK0VJotUk?BP07#bsMg=k%5hyJK drOhF9gO@`XpYEo)@2}ef0LTUmF_%h41vSi$^Pm6# diff --git a/PlatEMO/GUI/module_app.m b/PlatEMO/GUI/module_app.m index 9f014329a..90a180884 100644 --- a/PlatEMO/GUI/module_app.m +++ b/PlatEMO/GUI/module_app.m @@ -40,7 +40,7 @@ 'Constrained multi-objective optimization','Rotated optimization','Integer optimization','Binary optimization','Permutation optimization','Hybrid optimization'},'ItemsData',1:11,'Value',1,'Interruptible','off','BusyAction','cancel','ValueChangedFcn',@obj.cb_selectProblem)); % The second panel - obj.app.grid(2) = GUI.APP(3,1,uigridlayout(obj.app.maingrid,'RowHeight',num2cell([25,25,25,25,25,25,25,0,25,0,25,0,25,25,zeros(1,19),25,25,zeros(1,19),25,25,25,25,25,25]),'ColumnWidth',{75,50,'1x',25,20,20},'Padding',[0 10 0 0],'RowSpacing',5,'ColumnSpacing',5,'Scrollable','on','BackgroundColor','w')); + obj.app.grid(2) = GUI.APP(3,1,uigridlayout(obj.app.maingrid,'RowHeight',num2cell([25,25,25,25,25,25,25,0,25,0,25,0,25,25,zeros(1,19),25,25,zeros(1,19),25,25,25,25,25,25]),'ColumnWidth',{75,50,'1x',25,20,22},'Padding',[0 10 0 0],'RowSpacing',5,'ColumnSpacing',5,'Scrollable','on','BackgroundColor','w')); % Encoding scheme tempGrid = GUI.APP(1,[1 6],uigridlayout(obj.app.grid(2),'RowHeight',{'1x'},'ColumnWidth',{'1x'},'Padding',[0 0 0 6],'RowSpacing',0,'ColumnSpacing',5,'BackgroundColor','w')); obj.app.titleB(1) = GUI.APP(1,1,uilabel(tempGrid,'Text',' Encoding scheme','FontSize',13,'FontColor',[.9 .5 .2],'BackgroundColor',[.9 .9 .9],'FontWeight','bold')); @@ -55,51 +55,51 @@ obj.app.labelB(5) = GUI.APP(6,1,uilabel(obj.app.grid(2),'Text','Upper: x <=','HorizontalAlignment','right')); obj.app.editB(3) = GUI.APP(6,[2 5],uieditfield(obj.app.grid(2),'Value','1,1,1,1,1,1,1,1,1,1','Tooltip','Upper bound of each decision variable')); % Data - tempGrid = GUI.APP(7,[1 6],uigridlayout(obj.app.grid(2),'RowHeight',{'1x'},'ColumnWidth',{50,30,30,'1x'},'Padding',[0 0 0 6],'RowSpacing',0,'ColumnSpacing',5,'BackgroundColor','w')); + tempGrid = GUI.APP(7,[1 6],uigridlayout(obj.app.grid(2),'RowHeight',{'1x'},'ColumnWidth',{50,20,20,'1x'},'Padding',[0 0 0 6],'RowSpacing',0,'ColumnSpacing',5,'BackgroundColor','w')); obj.app.titleB(3) = GUI.APP(1,[1 4],uilabel(tempGrid,'Text',' Data','FontSize',13,'FontColor',[.9 .5 .2],'BackgroundColor',[.9 .9 .9],'FontWeight','bold')); - obj.app.buttonB(1) = GUI.APP(1,2,uibutton(tempGrid,'Text','Add','FontSize',10,'Tooltip','Add a data','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_updateProblem,1})); - obj.app.buttonB(2) = GUI.APP(1,3,uibutton(tempGrid,'Text','Del','Enable',false,'FontSize',10,'Tooltip','Delete the data','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_updateProblem,-1})); + obj.app.buttonB(1) = GUI.APP(1,2,uibutton(tempGrid,'Text','+','FontSize',11,'Tooltip','Add a data','BackgroundColor',[.9 .9 .9],'ButtonpushedFcn',{@obj.cb_updateProblem,1})); + obj.app.buttonB(2) = GUI.APP(1,3,uibutton(tempGrid,'Text','-','Enable',false,'FontSize',11,'Tooltip','Delete the data','BackgroundColor',[.9 .9 .9],'ButtonpushedFcn',{@obj.cb_updateProblem,-1})); obj.app.labelB(6) = GUI.APP(8,1,uilabel(obj.app.grid(2),'Text','data =','HorizontalAlignment','right')); obj.app.editB(4) = GUI.APP(8,[2 4],uieditfield(obj.app.grid(2),'Value','eye(10)','Tooltip','User-defined data of the problem')); obj.app.buttonB(3) = GUI.APP(8,5,uibutton(obj.app.grid(2),'Text','...','Tooltip','Load existing data','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_loadFunction,obj.app.editB(4),{'*.txt;*.dat;*.csv','Text file';'*.mat','MAT file'}})); % Initialization function - tempGrid = GUI.APP(9,[1 6],uigridlayout(obj.app.grid(2),'RowHeight',{'1x'},'ColumnWidth',{135,30,30,'1x'},'Padding',[0 0 0 6],'RowSpacing',0,'ColumnSpacing',5,'BackgroundColor','w')); + tempGrid = GUI.APP(9,[1 6],uigridlayout(obj.app.grid(2),'RowHeight',{'1x'},'ColumnWidth',{135,20,20,'1x'},'Padding',[0 0 0 6],'RowSpacing',0,'ColumnSpacing',5,'BackgroundColor','w')); obj.app.titleB(4) = GUI.APP(1,[1 4],uilabel(tempGrid,'Text',' Initialization function','FontSize',13,'FontColor',[.9 .5 .2],'BackgroundColor',[.9 .9 .9],'FontWeight','bold')); - obj.app.buttonB(4) = GUI.APP(1,2,uibutton(tempGrid,'Text','Add','FontSize',10,'Tooltip','Add an initialization function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_updateProblem,2})); - obj.app.buttonB(5) = GUI.APP(1,3,uibutton(tempGrid,'Text','Del','Enable',false,'FontSize',10,'Tooltip','Delete the initialization function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_updateProblem,-2})); + obj.app.buttonB(4) = GUI.APP(1,2,uibutton(tempGrid,'Text','+','FontSize',11,'Tooltip','Add an initialization function','BackgroundColor',[.9 .9 .9],'ButtonpushedFcn',{@obj.cb_updateProblem,2})); + obj.app.buttonB(5) = GUI.APP(1,3,uibutton(tempGrid,'Text','-','Enable',false,'FontSize',11,'Tooltip','Delete the initialization function','BackgroundColor',[.9 .9 .9],'ButtonpushedFcn',{@obj.cb_updateProblem,-2})); obj.app.labelB(7) = GUI.APP(10,1,uilabel(obj.app.grid(2),'Text','I(N) =','HorizontalAlignment','right')); obj.app.editB(5) = GUI.APP(10,[2 4],uieditfield(obj.app.grid(2),'Value','rand(N,10)','Tooltip','Function for generating an initial population with size N')); obj.app.buttonB(6) = GUI.APP(10,5,uibutton(obj.app.grid(2),'Text','...','Tooltip','Load existing function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_loadFunction,obj.app.editB(5),{'*.m','MATLAB function'}})); - obj.app.buttonB(7) = GUI.APP(10,6,uibutton(obj.app.grid(2),'Text','+','Fontsize',15,'Tooltip','Create new function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_createFunction,obj.app.editB(5),'initialization'})); + obj.app.buttonB(7) = GUI.APP(10,6,uibutton(obj.app.grid(2),'Text','','FontSize',9,'Tooltip','Create new function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_createFunction,obj.app.editB(5),'initialization'})); % Repair function - tempGrid = GUI.APP(11,[1 6],uigridlayout(obj.app.grid(2),'RowHeight',{'1x'},'ColumnWidth',{135,30,30,50,'1x'},'Padding',[0 0 0 6],'RowSpacing',0,'ColumnSpacing',5,'BackgroundColor','w')); + tempGrid = GUI.APP(11,[1 6],uigridlayout(obj.app.grid(2),'RowHeight',{'1x'},'ColumnWidth',{135,20,20,50,'1x'},'Padding',[0 0 0 6],'RowSpacing',0,'ColumnSpacing',5,'BackgroundColor','w')); obj.app.titleB(5) = GUI.APP(1,[1 5],uilabel(tempGrid,'Text',' Repair function','FontSize',13,'FontColor',[.9 .5 .2],'BackgroundColor',[.9 .9 .9],'FontWeight','bold')); - obj.app.buttonB(8) = GUI.APP(1,2,uibutton(tempGrid,'Text','Add','FontSize',10,'Tooltip','Add a repair function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_updateProblem,3})); - obj.app.buttonB(9) = GUI.APP(1,3,uibutton(tempGrid,'Text','Del','Enable',false,'FontSize',10,'Tooltip','Delete the repair function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_updateProblem,-3})); - obj.app.buttonB(10) = GUI.APP(1,4,uibutton(tempGrid,'Text','Integrate','FontSize',10,'Tooltip','Integrate the repair function, objective functions, and constraint functions into a single evaluation function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_updateProblem,6})); + obj.app.buttonB(8) = GUI.APP(1,2,uibutton(tempGrid,'Text','+','FontSize',11,'Tooltip','Add a repair function','BackgroundColor',[.9 .9 .9],'ButtonpushedFcn',{@obj.cb_updateProblem,3})); + obj.app.buttonB(9) = GUI.APP(1,3,uibutton(tempGrid,'Text','-','Enable',false,'FontSize',11,'Tooltip','Delete the repair function','BackgroundColor',[.9 .9 .9],'ButtonpushedFcn',{@obj.cb_updateProblem,-3})); + obj.app.buttonB(10) = GUI.APP(1,4,uibutton(tempGrid,'Text','Integrate','FontSize',10,'Tooltip','Integrate the repair function, objective functions, and constraint functions into a single evaluation function','BackgroundColor',[.9 .9 .9],'ButtonpushedFcn',{@obj.cb_updateProblem,6})); obj.app.labelB(8) = GUI.APP(12,1,uilabel(obj.app.grid(2),'Text','R(x) =','HorizontalAlignment','right')); obj.app.editB(6) = GUI.APP(12,[2 4],uieditfield(obj.app.grid(2),'Value','max(min(x,1),0)','Tooltip','Function for repairing invalid solution','UserData','')); obj.app.buttonB(11) = GUI.APP(12,5,uibutton(obj.app.grid(2),'Text','...','Tooltip','Load existing function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_loadFunction,obj.app.editB(6),{'*.m','MATLAB function'}})); - obj.app.buttonB(12) = GUI.APP(12,6,uibutton(obj.app.grid(2),'Text','+','Fontsize',15,'Tooltip','Create new function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_createFunction,obj.app.editB(6),'repair'})); + obj.app.buttonB(12) = GUI.APP(12,6,uibutton(obj.app.grid(2),'Text','','Fontsize',9,'Tooltip','Create new function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_createFunction,obj.app.editB(6),'repair'})); % Objective functions - tempGrid = GUI.APP(13,[1 6],uigridlayout(obj.app.grid(2),'RowHeight',{'1x'},'ColumnWidth',{135,30,30,'1x'},'Padding',[0 0 0 6],'RowSpacing',0,'ColumnSpacing',5,'BackgroundColor','w')); + tempGrid = GUI.APP(13,[1 6],uigridlayout(obj.app.grid(2),'RowHeight',{'1x'},'ColumnWidth',{135,20,20,'1x'},'Padding',[0 0 0 6],'RowSpacing',0,'ColumnSpacing',5,'BackgroundColor','w')); obj.app.titleB(6) = GUI.APP(1,[1 4],uilabel(tempGrid,'Text',' Objective functions','FontSize',13,'FontColor',[.9 .5 .2],'BackgroundColor',[.9 .9 .9],'FontWeight','bold')); - obj.app.buttonB(13) = GUI.APP(1,2,uibutton(tempGrid,'Text','Add','FontSize',10,'Tooltip','Add an objective function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_updateProblem,4})); - obj.app.buttonB(14) = GUI.APP(1,3,uibutton(tempGrid,'Text','Del','Enable',false,'FontSize',10,'Tooltip','Delete the last objective function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_updateProblem,-4})); + obj.app.buttonB(13) = GUI.APP(1,2,uibutton(tempGrid,'Text','+','FontSize',11,'Tooltip','Add an objective function','BackgroundColor',[.9 .9 .9],'ButtonpushedFcn',{@obj.cb_updateProblem,4})); + obj.app.buttonB(14) = GUI.APP(1,3,uibutton(tempGrid,'Text','-','Enable',false,'FontSize',11,'Tooltip','Delete the last objective function','BackgroundColor',[.9 .9 .9],'ButtonpushedFcn',{@obj.cb_updateProblem,-4})); obj.pro(1).label = GUI.APP(14,1,uilabel(obj.app.grid(2),'Text','f1(x) =','HorizontalAlignment','right')); obj.pro(1).edit = GUI.APP(14,[2 4],uieditfield(obj.app.grid(2),'Value','mean(x)','Tooltip','Objective function to be minimized')); obj.pro(1).button = GUI.APP(14,5,uibutton(obj.app.grid(2),'Text','...','Tooltip','Load existing function or data','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_loadFunction,obj.pro(1).edit,{'*.m','MATLAB function';'*.txt;*.dat;*.csv','Text file'}})); - obj.pro(1).button2 = GUI.APP(14,6,uibutton(obj.app.grid(2),'Text','+','Fontsize',15,'Tooltip','Create new function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_createFunction,obj.pro(1).edit,'objective'})); + obj.pro(1).button2 = GUI.APP(14,6,uibutton(obj.app.grid(2),'Text','','Fontsize',9,'Tooltip','Create new function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_createFunction,obj.pro(1).edit,'objective'})); % Constraint functions - tempGrid = GUI.APP(34,[1 6],uigridlayout(obj.app.grid(2),'RowHeight',{'1x'},'ColumnWidth',{135,30,30,'1x'},'Padding',[0 0 0 6],'RowSpacing',0,'ColumnSpacing',5,'BackgroundColor','w')); + tempGrid = GUI.APP(34,[1 6],uigridlayout(obj.app.grid(2),'RowHeight',{'1x'},'ColumnWidth',{135,20,20,'1x'},'Padding',[0 0 0 6],'RowSpacing',0,'ColumnSpacing',5,'BackgroundColor','w')); obj.app.titleB(7) = GUI.APP(1,[1 4],uilabel(tempGrid,'Text',' Constraint functions','FontSize',13,'FontColor',[.9 .5 .2],'BackgroundColor',[.9 .9 .9],'FontWeight','bold')); - obj.app.buttonB(15) = GUI.APP(1,2,uibutton(tempGrid,'Text','Add','FontSize',10,'Tooltip','Add a constraint function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_updateProblem,5})); - obj.app.buttonB(16) = GUI.APP(1,3,uibutton(tempGrid,'Text','Del','FontSize',10,'Tooltip','Delete the last constraint function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_updateProblem,-5})); + obj.app.buttonB(15) = GUI.APP(1,2,uibutton(tempGrid,'Text','+','FontSize',11,'Tooltip','Add a constraint function','BackgroundColor',[.9 .9 .9],'ButtonpushedFcn',{@obj.cb_updateProblem,5})); + obj.app.buttonB(16) = GUI.APP(1,3,uibutton(tempGrid,'Text','-','FontSize',11,'Tooltip','Delete the last constraint function','BackgroundColor',[.9 .9 .9],'ButtonpushedFcn',{@obj.cb_updateProblem,-5})); obj.con(1).labelA = GUI.APP(35,1,uilabel(obj.app.grid(2),'Text','g1(x) =','HorizontalAlignment','right')); obj.con(1).edit = GUI.APP(35,[2 3],uieditfield(obj.app.grid(2),'Value','0.5-mean(x)','Tooltip','Constraint function to be satisfied')); obj.con(1).labelB = GUI.APP(35,4,uilabel(obj.app.grid(2),'Text','<= 0')); obj.con(1).button = GUI.APP(35,5,uibutton(obj.app.grid(2),'Text','...','Tooltip','Load existing function or data','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_loadFunction,obj.con(1).edit,{'*.m','MATLAB function';'*.txt;*.dat;*.csv','Text file'}})); - obj.con(1).button2 = GUI.APP(35,6,uibutton(obj.app.grid(2),'Text','+','Fontsize',15,'Tooltip','Create new function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_createFunction,obj.con(1).edit,'constraint'})); + obj.con(1).button2 = GUI.APP(35,6,uibutton(obj.app.grid(2),'Text','','Fontsize',9,'Tooltip','Create new function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_createFunction,obj.con(1).edit,'constraint'})); % Special difficulties tempGrid = GUI.APP(55,[1 6],uigridlayout(obj.app.grid(2),'RowHeight',{'1x'},'ColumnWidth',{127,30,30,'1x'},'Padding',[0 0 0 6],'RowSpacing',0,'ColumnSpacing',5,'BackgroundColor','w')); obj.app.titleB(7) = GUI.APP(1,[1 4],uilabel(tempGrid,'Text',' Special difficulties','FontSize',13,'FontColor',[.9 .5 .2],'BackgroundColor',[.9 .9 .9],'FontWeight','bold')); @@ -109,32 +109,13 @@ obj.app.checkB(4) = GUI.APP(59,[1 6],uicheckbox(obj.app.grid(2),'FontSize',11,'Text',' The optimal solutions are sparse, i.e., most decision variables of which are zero.','WordWrap','on','ValueChangedFcn',@obj.cb_updateFilter)); % The third panel - obj.app.grid(3) = GUI.APP([2 3],4,uigridlayout(obj.app.maingrid,'RowHeight',{16,19,16,19,19,16,19,19,19,22,'1.2x','1x'},'ColumnWidth',{'1x','1x','1x'},'Padding',[8 10 8 0],'RowSpacing',3,'ColumnSpacing',5,'BackgroundColor','w')); - obj.app.labelC(1) = GUI.APP(1,[1 3],uilabel(obj.app.grid(3),'Text','Number of objectives','VerticalAlignment','bottom','FontSize',12,'FontColor',[.15 .6 .2],'FontWeight','bold')); - obj.app.stateC(1) = GUI.APP(2,1,uibutton(obj.app.grid(3),'state','Text','single','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Tooltip','The problem has a single objective','ValueChangedFcn',{@obj.cb_filter,1})); - obj.app.stateC(2) = GUI.APP(2,2,uibutton(obj.app.grid(3),'state','Text','multi','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Tooltip','The problem has 2 or 3 objectives','ValueChangedFcn',{@obj.cb_filter,2})); - obj.app.stateC(3) = GUI.APP(2,3,uibutton(obj.app.grid(3),'state','Text','many','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Tooltip','The problem has more than 3 objectives','ValueChangedFcn',{@obj.cb_filter,3})); - obj.app.labelC(2) = GUI.APP(3,[1 3],uilabel(obj.app.grid(3),'Text','Encoding scheme','VerticalAlignment','bottom','FontSize',12,'FontColor',[.15 .6 .2],'FontWeight','bold')); - obj.app.stateC(4) = GUI.APP(4,1,uibutton(obj.app.grid(3),'state','Text','real','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Tooltip','The decision variables are real numbers','ValueChangedFcn',{@obj.cb_filter,4})); - obj.app.stateC(5) = GUI.APP(4,2,uibutton(obj.app.grid(3),'state','Text','integer','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Tooltip','The decision variables are integers','ValueChangedFcn',{@obj.cb_filter,5})); - obj.app.stateC(6) = GUI.APP(4,3,uibutton(obj.app.grid(3),'state','Text','label','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Tooltip','The decision variables are labels','ValueChangedFcn',{@obj.cb_filter,6})); - obj.app.stateC(7) = GUI.APP(5,1,uibutton(obj.app.grid(3),'state','Text','binary','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Tooltip','The decision variables are binary numbers','ValueChangedFcn',{@obj.cb_filter,7})); - obj.app.stateC(8) = GUI.APP(5,2,uibutton(obj.app.grid(3),'state','Text','permutation','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Tooltip','The decision vector is a permutation','ValueChangedFcn',{@obj.cb_filter,8})); - obj.app.labelC(3) = GUI.APP(6,[1 3],uilabel(obj.app.grid(3),'Text','Special difficulties','VerticalAlignment','bottom','FontSize',12,'FontColor',[.15 .6 .2],'FontWeight','bold')); - obj.app.stateC(9) = GUI.APP(7,1,uibutton(obj.app.grid(3),'state','Text','large','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Tooltip','The problem has more than 100 decision variables','ValueChangedFcn',{@obj.cb_filter,9})); - obj.app.stateC(10) = GUI.APP(7,2,uibutton(obj.app.grid(3),'state','Text','constrained','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Tooltip','The problem has constraints','ValueChangedFcn',{@obj.cb_filter,10})); - obj.app.stateC(11) = GUI.APP(7,3,uibutton(obj.app.grid(3),'state','Text','expensive','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Tooltip','The objectives are computationally time-consuming','ValueChangedFcn',{@obj.cb_filter,11})); - obj.app.stateC(12) = GUI.APP(8,1,uibutton(obj.app.grid(3),'state','Text','multimodal','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Tooltip','The objectives are multimodal','ValueChangedFcn',{@obj.cb_filter,12})); - obj.app.stateC(13) = GUI.APP(8,2,uibutton(obj.app.grid(3),'state','Text','sparse','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Tooltip','Most decision variables of the optimal solutions are zero','ValueChangedFcn',{@obj.cb_filter,13})); - obj.app.stateC(14) = GUI.APP(8,3,uibutton(obj.app.grid(3),'state','Text','dynamic','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Tooltip','The objectives vary periodically','ValueChangedFcn',{@obj.cb_filter,14})); - obj.app.stateC(15) = GUI.APP(9,1,uibutton(obj.app.grid(3),'state','Text','multitask','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Tooltip','The problem has multiple tasks to be solved simultaneously','ValueChangedFcn',{@obj.cb_filter,15})); - obj.app.stateC(16) = GUI.APP(9,2,uibutton(obj.app.grid(3),'state','Text','bilevel','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Tooltip','The problem has two nested objectives','ValueChangedFcn',{@obj.cb_filter,16})); - obj.app.stateC(17) = GUI.APP(9,3,uibutton(obj.app.grid(3),'state','Text','robust','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Tooltip','The objectives are influenced by uncertain factors','ValueChangedFcn',{@obj.cb_filter,17})); - obj.app.labelC(4) = GUI.APP(10,[1 2],uilabel(obj.app.grid(3),'Text','Algorithms','VerticalAlignment','bottom','FontSize',13,'FontColor',[.2 .4 .7],'FontWeight','bold')); - obj.app.labelC(5) = GUI.APP(10,3,uilabel(obj.app.grid(3),'HorizontalAlignment','right','VerticalAlignment','bottom','FontSize',10,'FontColor',[.2 .4 .7])); - obj.app.listC = GUI.APP(11,[1 3],uilistbox(obj.app.grid(3),'FontColor',[.2 .4 .7],'ValueChangedFcn',{@obj.cb_updateList,3})); - obj.app.listD = uilist(obj.app.grid(3),obj.GUI.app.figure,obj.GUI.icon); - obj.app.tipD = GUI.APP(12,[1 3],uilabel(obj.app.grid(3),'Text','Select an algorithm','HorizontalAlignment','center')); + obj.app.grid(3) = GUI.APP([2 3],4,uigridlayout(obj.app.maingrid,'RowHeight',{16,19,16,19,19,16,19,19,19,22,'1.1x','1x'},'ColumnWidth',{'1x','1x','1x'},'Padding',[8 10 8 0],'RowSpacing',3,'ColumnSpacing',5,'BackgroundColor','w')); + [obj.app.stateC,obj.app.labelC] = GUI.GenerateLabelButton(obj.app.grid(3),zeros(1,17),@obj.cb_filter); + obj.app.labelC(4) = GUI.APP(10,[1 2],uilabel(obj.app.grid(3),'Text','Algorithms','VerticalAlignment','bottom','FontSize',13,'FontColor',[.2 .4 .7],'FontWeight','bold')); + obj.app.labelC(5) = GUI.APP(10,3,uilabel(obj.app.grid(3),'HorizontalAlignment','right','VerticalAlignment','bottom','FontSize',10,'FontColor',[.2 .4 .7])); + obj.app.listC = GUI.APP(11,[1 3],uilistbox(obj.app.grid(3),'FontColor',[.2 .4 .7],'ValueChangedFcn',@obj.cb_updateList)); + obj.app.listD = uilist(obj.app.grid(3),obj.GUI.app.figure,obj.GUI.icon); + obj.app.tipD = GUI.APP(12,[1 3],uilabel(obj.app.grid(3),'Text','Select an algorithm','HorizontalAlignment','center')); obj.app.listD.grid.Padding = [0,0,0,0]; GUI.APP(12,[1 3],obj.app.listD.grid); @@ -144,13 +125,13 @@ obj.app.grid(4) = GUI.APP([2 3],[6 8],uigridlayout(obj.app.maingrid,'RowHeight',{'1x',40,30},'ColumnWidth',{20,150,'1x','1x',120,30,20},'Padding',[15 10 15 0],'RowSpacing',5,'BackgroundColor','w')); obj.app.axes = GUI.APP(1,[2 6],uiaxes(obj.app.grid(4),'BackgroundColor','w','Box','on')); obj.app.waittip = GUI.APP(1,[2 6],uilabel(obj.app.grid(4),'HorizontalAlignment','center','Text',' Please wait ... ...','Visible',false)); - tempTb = axtoolbar(obj.app.axes(1),{'rotate','pan','zoomin','zoomout'}); + tempTb = axtoolbar(obj.app.axes,{'rotate','pan','zoomin','zoomout'}); obj.app.toolD(1) = axtoolbarbtn(tempTb,'push','Icon',obj.GUI.icon.gif,'Tooltip','Save the evolutionary process to gif','ButtonPushedFcn',@obj.cb_toolbutton1); obj.app.toolD(2) = axtoolbarbtn(tempTb,'push','Icon',obj.GUI.icon.newfigure,'Tooltip','Open in new figure and save to workspace','ButtonPushedFcn',@obj.cb_toolbutton2); obj.app.slider = GUI.APP(2,[1 7],uislider(obj.app.grid(4),'Limits',[0 1],'MajorTicks',0:0.25:1,'MajorTickLabels',{'0%','25%','50%','75%','100%'},'MinorTicks',0:0.01:1,'ValueChangedFcn',@obj.cb_slider)); obj.app.labelD = GUI.APP(3,[1 2],uilabel(obj.app.grid(4),'Text','','HorizontalAlignment','left')); obj.app.buttonD(1) = GUI.APP(3,3,uibutton(obj.app.grid(4),'push','Text','Start','FontSize',16,'Enable',false,'ButtonpushedFcn',@obj.cb_start)); - obj.app.buttonD(2) = GUI.APP(3,4,uibutton(obj.app.grid(4),'push','Text','Stop','FontSize',16,'Enable',false,'ButtonpushedFcn',@obj.cb_stop)); + obj.app.buttonD(2) = GUI.APP(3,4,uibutton(obj.app.grid(4),'push','Text','Stop','FontSize',16,'Enable',false,'ButtonpushedFcn',@(~,~)set(obj.app.buttonD(1:2),{'Enable','Text'},{true,'Start';false,'Stop'}))); obj.app.menuD = uicontext(obj.GUI.app.figure,120); obj.app.menuD.add(' Save best solutions','',{@obj.cb_save,1}); obj.app.menuD.add(' Save all solutions','',{@obj.cb_save,2}); @@ -159,30 +140,19 @@ % Initialization obj.cb_updateFilter(); - obj.cb_filter(); + obj.cb_filter([],[],0); end end methods(Access = private) - %% Update the algorithms in the lists + %% Update the algorithms in the list function cb_filter(obj,~,~,index) - if nargin > 3 - if index < 4 - [obj.app.stateC(1:3).Value] = deal(0); - obj.app.stateC(index).Value = 1; - end - end - filter = [obj.app.stateC.Value]; - func = @(s)all(any(repmat([true,filter],size(s,1),1)&s,2)) && all((any(s(:,2:end),1)&filter)==filter); % Update the list of algorithms - show = cellfun(func,obj.GUI.algList(:,1)); - obj.app.listC.Items = ['(Open File)';obj.GUI.algList(show,2)]; - obj.app.listC.Value = {}; - obj.app.labelC(5).Text = sprintf('%d / %d',sum(show),length(show)); + func = GUI.UpdateAlgProList(index,obj.app.stateC,obj.app.listC,obj.app.labelC(5),obj.GUI.algList); obj.app.listD.del(1); - obj.app.tipD.Visible = 'on'; - obj.app.buttonD(1).Enable = 'off'; + obj.app.tipD.Visible = true; + obj.app.buttonD(1).Enable = false; % Update the list of metrics - show = cellfun(@(s)func(s(2:end,1:end-2)),obj.GUI.metList(:,1)); + show = cellfun(@(s)func(s(2:end,1:end-2)),obj.GUI.metList(:,1)); if obj.app.stateC(1).Value == 0 % Multi-objective optimization obj.app.dropD(1).Items = ['Population (objectives)';'Population (variables)';obj.GUI.metList(show,2)]; else % Single-objective optimization @@ -190,33 +160,10 @@ function cb_filter(obj,~,~,index) end end %% Update the parameter list - function cb_updateList(obj,~,~,type) - filename = obj.app.listC.Value; - if contains(filename,'Open File') - [file,path] = uigetfile({'*.m','MATLAB class'},''); - if file ~= 0 - try - filename = fullfile(path,file); - f = fopen(filename); - str = fgetl(f); - fclose(f); - assert(contains(str,'< ALGORITHM')); - addpath(path); - catch - uialert(obj.GUI.app.figure,'The selected file is not a subclass of ALGORITHM.','Error'); - return; - end - else - return; - end - else - filename = [filename,'.m']; - end - obj.app.tipD.Visible = 'off'; - obj.app.buttonD(1).Enable = 'on'; - obj.app.listD.del(1); - obj.app.listD.add(filename,type); - obj.app.listD.flush(); + function cb_updateList(obj,~,~) + GUI.UpdateAlgProPara(obj.GUI.app.figure,obj.app.listC,obj.app.listD,'ALGORITHM',3); + obj.app.tipD.Visible = false; + obj.app.buttonD(1).Enable = true; end %% Update the problem definition panel function cb_updateProblem(obj,~,~,index,noFlush) @@ -256,7 +203,7 @@ function cb_updateProblem(obj,~,~,index,noFlush) item.label = GUI.APP(length(obj.pro)+14,1,uilabel(obj.app.grid(2),'Text',sprintf('f%d(x%s) =',length(obj.pro)+1,str{1}),'HorizontalAlignment','right')); item.edit = GUI.APP(length(obj.pro)+14,[2 4],uieditfield(obj.app.grid(2),'Value',sprintf('mean(x%s)',str{2}),'Tooltip','Objective function to be minimized')); item.button = GUI.APP(length(obj.pro)+14,5,uibutton(obj.app.grid(2),'Text','...','Tooltip','Load existing function or data','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_loadFunction,item.edit,{'*.m','MATLAB function';'*.txt;*.dat;*.csv','Text file'}})); - item.button2 = GUI.APP(length(obj.pro)+14,6,uibutton(obj.app.grid(2),'Text','+','Fontsize',15,'Tooltip','Create new function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_createFunction,item.edit,'objective'})); + item.button2 = GUI.APP(length(obj.pro)+14,6,uibutton(obj.app.grid(2),'Text','','Fontsize',9,'Tooltip','Create new function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_createFunction,item.edit,'objective'})); obj.pro = [obj.pro,item]; elseif index<0 && length(obj.pro)>0 delete(obj.pro(end).label); @@ -279,7 +226,7 @@ function cb_updateProblem(obj,~,~,index,noFlush) item.edit = GUI.APP(length(obj.con)+35,[2 3],uieditfield(obj.app.grid(2),'Value',sprintf('0.5-mean(x%s)',str{2}),'Tooltip','Constraint function to be satisfied')); item.labelB = GUI.APP(length(obj.con)+35,4,uilabel(obj.app.grid(2),'Text','<= 0')); item.button = GUI.APP(length(obj.con)+35,5,uibutton(obj.app.grid(2),'Text','...','Tooltip','Load existing function or data','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_loadFunction,item.edit,{'*.m','MATLAB function';'*.txt;*.dat;*.csv','Text file'}})); - item.button2 = GUI.APP(length(obj.con)+35,6,uibutton(obj.app.grid(2),'Text','+','Fontsize',15,'Tooltip','Create new function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_createFunction,item.edit,'constraint'})); + item.button2 = GUI.APP(length(obj.con)+35,6,uibutton(obj.app.grid(2),'Text','','Fontsize',9,'Tooltip','Create new function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_createFunction,item.edit,'constraint'})); obj.con = [obj.con,item]; elseif index<0 && length(obj.con)>0 delete(obj.con(end).labelA); @@ -350,15 +297,15 @@ function cb_updateProblem(obj,~,~,index,noFlush) uialert(obj.GUI.app.figure,'The problem definition is valid.','Valid problem','Icon','success'); end end - %% Define a problem - function cb_selectProblem(obj,~,~) - if obj.app.dropA.Value == 1 - if ~isempty(obj.app.dropA.UserData) - GetSetProblem(obj,obj.app.dropA.UserData); - obj.app.dropA.UserData = []; + %% Select a predefined a problem + function cb_selectProblem(obj,h,~) + if h.Value == 1 + if ~isempty(h.UserData) + GetSetProblem(obj,h.UserData); + h.UserData = []; end else - switch obj.app.dropA.Value + switch h.Value case 2 Str = {'ones(1,10)','zeros(1,10)','ones(1,10)','','','','mean(x.^2)',''}; case 3 @@ -380,8 +327,8 @@ function cb_selectProblem(obj,~,~) case 11 Str = {'1,1,1,2,2,2,4,4,4','zeros(1,9)','zeros(1,9)+10','','','','sum((x-pi).^2)',''}; end - if isempty(obj.app.dropA.UserData) - obj.app.dropA.UserData = GetSetProblem(obj); + if isempty(h.UserData) + h.UserData = GetSetProblem(obj); end GetSetProblem(obj,Str); end @@ -389,6 +336,7 @@ function cb_selectProblem(obj,~,~) %% Load a problem function cb_loadProblem(obj,~,~) [file,folder] = uigetfile({'*.m','MATLAB code'}); + figure(obj.GUI.app.figure); if ischar(file) try f = fopen(fullfile(folder,file)); @@ -417,6 +365,7 @@ function cb_loadProblem(obj,~,~) %% Save the problem function cb_saveProblem(obj,~,~) [Name,Path] = uiputfile({'*.m','MATLAB code'},'','new'); + figure(obj.GUI.app.figure); if ischar(Name) try Str = GetSetProblem(obj); @@ -450,6 +399,7 @@ function cb_saveProblem(obj,~,~) fprintf(fid,'%s\n',Code{i}); end fclose(fid); + web(['file://',fullfile(Path,Name)],'-browser'); catch err uialert(obj.GUI.app.figure,'Fail to save the problem, please refer to the command window for details.','Error'); rethrow(err); @@ -463,6 +413,7 @@ function cb_loadFunction(obj,~,~,h,ext) else [file,folder] = uigetfile(ext,'',cd); end + figure(obj.GUI.app.figure); if file ~= 0 h.Value = sprintf('<%s>',fullfile(folder,file)); end @@ -474,6 +425,7 @@ function cb_createFunction(obj,~,~,h,type) else [file,folder] = uiputfile({'*.m','MATLAB function'},'',fullfile(cd,'myFcn')); end + figure(obj.GUI.app.figure); if ischar(file) try fid = fopen(fullfile(folder,file),'wt'); @@ -583,7 +535,7 @@ function cb_updateFilter(obj,~,~) obj.app.stateC(12).Value = obj.app.checkB(3).Value; obj.app.stateC(13).Value = obj.app.checkB(4).Value; if any(oldState~=[obj.app.stateC.Value]) - obj.cb_filter(); + obj.cb_filter([],[],0); end end %% Start the execution @@ -595,38 +547,22 @@ function cb_start(obj,~,~) else % Generate the ALGORITHM object try - item = obj.app.listD.items(1); - para = cell(1,length(item.edit)); - for j = 1 : length(para) - if ~isempty(item.edit(j).Value) - para{j} = str2num(item.edit(j).Value); - assert(~isempty(para{j}),'The parameter "%s" of %s is illegal.',item.label(j).Text,item.title.Text); - end - end - ALG = feval(item.title.Text,'parameter',para(3:end),'outputFcn',@obj.outputFcn,'save',inf); + [name,para] = GUI.GetParameterSetting(obj.app.listD.items(1)); + ALG = feval(name,'parameter',para(3:end),'outputFcn',@obj.outputFcn,'save',20); catch err uialert(obj.GUI.app.figure,err.message,'Invalid parameter settings'); return; end % Generate the PROBLEM object - PRO = obj.cb_validation(para(1:2),item.label(2).Text); + PRO = obj.cb_validation(para(1:2),obj.app.listD.items(1).label(2).Text); % Update the data obj.data = {ALG,PRO,{obj.app.buttonB.Enable}}; % Update the GUI - [obj.GUI.app.button.Enable] = deal('off'); - [obj.app.buttonA.Enable] = deal('off'); - obj.app.dropA.Enable = 'off'; - obj.app.editB(1).Enable = 'off'; - [obj.app.editB(2:end).Enable] = deal('off'); - [obj.app.buttonB.Enable] = deal('off'); - [obj.app.checkB.Enable] = deal('off'); - [obj.app.stateC.Enable] = deal('off'); - obj.app.listC.Enable = 'off'; - obj.app.listD.Enable = 'off'; - [obj.app.toolD(1:2).Visible] = deal('off'); - obj.app.buttonD(1).Text = 'Pause'; - obj.app.buttonD(2).Enable = 'on'; - obj.app.buttonD(3).Enable = 'on'; + set([obj.GUI.app.button,obj.app.buttonA,obj.app.dropA,obj.app.editB,obj.app.buttonB,obj.app.checkB,obj.app.stateC,obj.app.listC,obj.app.dropD],'Enable',false); + obj.app.listD.Enable = false; + set(obj.app.toolD,'Visible',false); + obj.app.buttonD(1).Text = 'Pause'; + set(obj.app.buttonD([2,3]),'Enable',true); if PRO.M > 1 obj.app.dropD(1).Value = obj.app.dropD(1).Items{1}; obj.app.dropD(1).Visible = 'on'; @@ -636,10 +572,7 @@ function cb_start(obj,~,~) obj.app.dropD(1).Visible = 'off'; obj.app.dropD(2).Visible = 'on'; end - obj.app.dropD(1).Enable = 'off'; - obj.app.dropD(2).Enable = 'off'; - set([obj.pro.edit,obj.con.edit],'Enable','off'); - set([obj.pro.button,obj.pro.button2,obj.con.button,obj.con.button2],'Enable','off'); + set([obj.pro.edit,obj.con.edit,obj.pro.button,obj.pro.button2,obj.con.button,obj.con.button2],'Enable',false); % Execute the algorithm try ALG.Solve(PRO); @@ -653,23 +586,17 @@ function cb_start(obj,~,~) end %% Stop the execution function cb_stop(obj,~,~) - [obj.GUI.app.button.Enable] = deal('on'); - [obj.app.buttonA.Enable] = deal('on'); - obj.app.dropA.Enable = 'on'; - obj.app.editB(1).Enable = 'on'; - [obj.app.editB(2:end).Enable] = deal('on'); - [obj.app.buttonB.Enable] = deal(obj.data{3}{:}); - [obj.app.checkB.Enable] = deal('on'); - [obj.app.stateC.Enable] = deal('on'); - obj.app.listC.Enable = 'on'; - obj.app.listD.Enable = 'on'; - [obj.app.toolD(1:2).Visible] = deal('on'); - obj.app.buttonD(1).Text = 'Start'; - obj.app.buttonD(2).Enable = 'off'; - obj.app.dropD(1).Enable = 'on'; - obj.app.dropD(2).Enable = 'on'; - set([obj.pro.edit,obj.con.edit],'Enable','on'); - set([obj.pro.button,obj.pro.button2,obj.con.button,obj.con.button2],'Enable','on'); + set([obj.GUI.app.button,obj.app.buttonA,obj.app.dropA,obj.app.editB,obj.app.checkB,obj.app.stateC,obj.app.listC,obj.app.dropD],'Enable',true); + obj.app.listD.Enable = true; + [obj.app.buttonB.Enable] = deal(obj.data{3}{:}); + obj.app.listD.Enable = true; + set(obj.app.toolD,'Visible',true); + obj.app.buttonD(1).Text = 'Start'; + obj.app.buttonD(2).Enable = false; + set([obj.pro.edit,obj.con.edit,obj.pro.button,obj.pro.button2,obj.con.button,obj.con.button2],'Enable',true); + if isempty(obj.data{1}.result) + obj.data = {}; + end end %% Save the result function cb_save(obj,~,~,type) @@ -677,30 +604,7 @@ function cb_save(obj,~,~,type) PRO = obj.data{2}; rate = PRO.FE/max(PRO.FE,PRO.maxFE); index = max(1,round(obj.app.slider.Value/rate*size(ALG.result,1))); - Pop = ALG.result{index,2}; - if type == 1 - Pop = Pop.best; - end - if isempty(Pop) - uialert(obj.GUI.app.figure,'No solution can be saved.','Error'); - else - Data = [Pop.decs,Pop.objs,Pop.cons]; - try - [Name,Path] = uiputfile({'*.txt','Text file';'*.dat','Text file';'*.csv','Text file';'*.mat','MAT file';'*.xlsx','Excel table'},'','data'); - if ischar(Name) - [~,~,Type] = fileparts(Name); - switch Type - case '.mat' - save(fullfile(Path,Name),'Data','-mat'); - otherwise - writematrix(Data,fullfile(Path,Name)); - end - end - catch err - uialert(obj.GUI.app.figure,'Fail to save the result, please refer to the command window for details.','Error'); - rethrow(err); - end - end + GUI.SavePopulation(obj.GUI.app.figure,ALG.result{index,2},type); end %% Output function function outputFcn(obj,Algorithm,Problem) @@ -757,6 +661,7 @@ function cb_slider(obj,~,~,ax) function cb_toolbutton1(obj,~,~) if ~isempty(obj.data) [file,folder] = uiputfile({'*.gif','GIF image'},''); + figure(obj.GUI.app.figure); if file ~= 0 try filename = fullfile(folder,file); diff --git a/PlatEMO/GUI/module_cre.m b/PlatEMO/GUI/module_cre.m index b4d3bb542..c5dd7ca05 100644 --- a/PlatEMO/GUI/module_cre.m +++ b/PlatEMO/GUI/module_cre.m @@ -2,7 +2,7 @@ %module_cre - Creation module. %------------------------------- Copyright -------------------------------- -% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for % research purposes. All publications which use this platform or any code % in the platform should acknowledge the use of "PlatEMO" and reference "Ye % Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform @@ -11,19 +11,1159 @@ %-------------------------------------------------------------------------- properties(SetAccess = private) - GUI; % The GUI object - app = struct(); % All the components - Blocks; % All blocks - Graph; % Adjacent matrix - Boxes; % Boxes for all blocks - Lines; % Lines for all edges + GUI; % The GUI object + app = struct(); % All the components + blockList; % List of candidate blocks + Graph = digraph(); % All blocks and lines + data = {}; % All the results + dataTrain = {}; % All the results of training end methods(Access = ?GUI) %% Constructor function obj = module_cre(GUI) % The main grid obj.GUI = GUI; - obj.app.maingrid = GUI.APP(3,1,uilabel(obj.GUI.app.maingrid,'Text','Under development','HorizontalAlignment','center')); + obj.app.maingrid = GUI.APP(3,1,uigridlayout(obj.GUI.app.maingrid,'RowHeight',{20,30,'1.2x',1,15,'1x'},'ColumnWidth',{'3x',5,1,'1.1x',1,5,'1.5x'},'Padding',[5 5 5 5],'RowSpacing',5,'ColumnSpacing',0,'BackgroundColor','w')); + obj.app.label(1) = GUI.APP(1,1,uilabel(obj.app.maingrid,'Text','Algorithm creation','HorizontalAlignment','center','FontSize',11,'FontColor',[.5 .5 .5])); + obj.app.label(2) = GUI.APP(1,4,uilabel(obj.app.maingrid,'Text','Problem selection','HorizontalAlignment','center','FontSize',11,'FontColor',[.5 .5 .5])); + obj.app.label(3) = GUI.APP(1,7,uilabel(obj.app.maingrid,'Text','Training','HorizontalAlignment','center','FontSize',11,'FontColor',[.5 .5 .5])); + obj.app.label(4) = GUI.APP(5,7,uilabel(obj.app.maingrid,'Text','Testing','HorizontalAlignment','center','FontSize',11,'FontColor',[.5 .5 .5])); + GUI.APP([1 6],3,uipanel(obj.app.maingrid,'BackgroundColor',[.8 .8 .8])); + GUI.APP([1 6],5,uipanel(obj.app.maingrid,'BackgroundColor',[.8 .8 .8])); + GUI.APP(4,7,uipanel(obj.app.maingrid,'BackgroundColor',[.8 .8 .8])); + + % The first panel + obj.app.loadmenu = uicontext(obj.GUI.app.figure,115); + obj.app.loadmenu.add(' Load a block','',@obj.cb_loadblock); + obj.app.loadmenu.add(' Load an algorithm','',@obj.cb_loadalgorithm); + obj.app.loadmenu.flush(); + obj.app.grid(1) = GUI.APP(2,1,uigridlayout(obj.app.maingrid,'RowHeight',{1,'1x',1},'ColumnWidth',{18,24,18,18,1,18,18,'1x',70,'0.9x'},'Padding',[10 5 10 5],'RowSpacing',0,'ColumnSpacing',7,'BackgroundColor',[.95 .95 1])); + tempPanel = GUI.APP(2,1,uipanel(obj.app.grid(1),'BorderType','none','BackgroundColor',[.95 .95 1])); + obj.app.buttonA(1) = uibutton(tempPanel,'Position',[-2.5 -2.5 24 24],'Text','','Icon',obj.GUI.icon.block,'BackgroundColor',[.95 .95 1],'Tooltip','Add a block','Interruptible','off','BusyAction','cancel','ButtonpushedFcn',@obj.cb_addblock); + tempPanel = GUI.APP(2,2,uipanel(obj.app.grid(1),'BorderType','none','BackgroundColor',[.95 .95 1])); + obj.app.buttonA(2) = uibutton(tempPanel,'Position',[-2.5 -2.5 31 24],'Text','','Icon',obj.GUI.icon.loadtable2,'BackgroundColor',[.95 .95 1],'Tooltip','Load a block or algorithm','Interruptible','off','BusyAction','cancel','ButtonpushedFcn',@(~,~)obj.app.loadmenu.show()); + tempPanel = GUI.APP(2,3,uipanel(obj.app.grid(1),'BorderType','none','BackgroundColor',[.95 .95 1])); + obj.app.buttonA(3) = uibutton(tempPanel,'Position',[-2.5 -2.5 24 24],'Text','','Icon',obj.GUI.icon.savetable,'BackgroundColor',[.95 .95 1],'Tooltip','Save the algorithm','Interruptible','off','BusyAction','cancel','ButtonpushedFcn',@obj.cb_savealgorithm); + tempPanel = GUI.APP(2,4,uipanel(obj.app.grid(1),'BorderType','none','BackgroundColor',[.95 .95 1])); + obj.app.buttonA(4) = uibutton(tempPanel,'Position',[-2.5 -2.5 24 24],'Text','','Icon',obj.GUI.icon.code,'BackgroundColor',[.95 .95 1],'Tooltip','Generate code','Interruptible','off','BusyAction','cancel','ButtonpushedFcn',@obj.cb_generatecode); + GUI.APP([1 3],5,uipanel(obj.app.grid(1),'BackgroundColor',[.8 .8 .8])); + tempPanel = GUI.APP(2,6,uipanel(obj.app.grid(1),'BorderType','none','BackgroundColor',[.95 .95 1])); + obj.app.buttonA(5) = uibutton(tempPanel,'Position',[-2.5 -2.5 24 24],'Text','','Icon',obj.GUI.icon.feature,'BackgroundColor',[.95 .95 1],'Tooltip','Select features to display','Interruptible','off','BusyAction','cancel','ButtonpushedFcn',@obj.cb_selectfeature); + tempPanel = GUI.APP(2,7,uipanel(obj.app.grid(1),'BorderType','none','BackgroundColor',[.95 .95 1])); + obj.app.buttonA(6) = uibutton(tempPanel,'Position',[-2.5 -2.5 24 24],'Text','','Icon',obj.GUI.icon.arrange,'BackgroundColor',[.95 .95 1],'Tooltip','Arrange the blocks','Interruptible','off','BusyAction','cancel','ButtonpushedFcn',@obj.cb_arrange); + obj.app.buttonA(7) = GUI.APP([1 3],9,uibutton(obj.app.grid(1),'Text','Validation','BackgroundColor',[.95 .95 1],'Tooltip','Check the validity of the algorithm','Interruptible','off','BusyAction','cancel','ButtonpushedFcn',@obj.cb_validation)); + obj.app.dropA = GUI.APP([1 3],10,uidropdown(obj.app.grid(1),'BackgroundColor',[.95 .95 1],'Tooltip','Select predefined examples','Items',{'User-defined algorithm','General algorithm','Coevolutionary algorithm','Complex algorithm 1','Complex algorithm 2'},'ItemsData',1:5,'Value',1,'Interruptible','off','BusyAction','cancel','ValueChangedFcn',@obj.cb_selectAlgorithm)); + + % The second panel + obj.app.grid(2) = GUI.APP([3 6],1,uigridlayout(obj.app.maingrid,'RowHeight',{'1x'},'ColumnWidth',{'1x'},'Padding',[0 0 15 10],'BackgroundColor',[.95 .95 1],'RowSpacing',0,'ColumnSpacing',0)); + obj.app.canvas = GUI.APP(1,1,uiaxes(obj.app.grid(2),'NextPlot','add','ClippingStyle','rectangle','XColor','none','YColor','none','view',[0 90],'XLim',[0 100],'YLim',[0 100],'ButtonDownFcn',@(h,~)HighlightObject(h))); + obj.app.menu = uicontext(obj.GUI.app.figure,85); + obj.app.menu.add('Details',obj.GUI.icon.feature,@obj.cb_checkdetail); + obj.app.menu.add('Color',obj.GUI.icon.color,@obj.cb_setcolor); + obj.app.menu.add('Open file',obj.GUI.icon.file,@obj.cb_openfile); + obj.app.menu.add('Save block',obj.GUI.icon.savetable,@obj.cb_saveblock); + obj.app.menu.add('Delete',obj.GUI.icon.delete,@obj.cb_delete); + set(obj.app.menu.gaps([2,4]),'Visible',false); + obj.app.menu.flush(); + + % The third panel + obj.app.grid(3) = GUI.APP([2 6],4,uigridlayout(obj.app.maingrid,'RowHeight',{16,19,16,19,19,16,19,19,19,22,'1.1x','1x'},'ColumnWidth',{'1x','1x','1x'},'Padding',[8 10 8 0],'RowSpacing',3,'ColumnSpacing',5,'BackgroundColor','w')); + [obj.app.stateC,obj.app.labelC] = GUI.GenerateLabelButton(obj.app.grid(3),[1 0 0 1,zeros(1,13)],@obj.cb_filter); + obj.app.labelC(4) = GUI.APP(10,[1 2],uilabel(obj.app.grid(3),'Text','Problems','VerticalAlignment','bottom','FontSize',13,'FontColor',[.9 .5 .2],'FontWeight','bold')); + obj.app.labelC(5) = GUI.APP(10,3,uilabel(obj.app.grid(3),'HorizontalAlignment','right','VerticalAlignment','bottom','FontSize',10,'FontColor',[.9 .5 .2])); + obj.app.listC = GUI.APP(11,[1 3],uilistbox(obj.app.grid(3),'FontColor',[.9 .5 .2])); + obj.app.listD = uilist(obj.app.grid(3),obj.GUI.app.figure,obj.GUI.icon); + obj.app.listC.ValueChangedFcn = @(~,~)GUI.UpdateAlgProPara(obj.GUI.app.figure,obj.app.listC,obj.app.listD,'PROBLEM',-1); + obj.app.listD.grid.Padding = [0,0,0,0]; + GUI.APP(12,[1 3],obj.app.listD.grid); + + % The fourth panel + obj.app.grid(4) = GUI.APP([2 3],7,uigridlayout(obj.app.maingrid,'RowHeight',{22,22,22,'1x',26},'ColumnWidth',{'1x',20,'1x',5,'1x',20,'1x'},'Padding',[5 5 5 0],'BackgroundColor','w','RowSpacing',5,'ColumnSpacing',5)); + GUI.APP(1,[1 2],uilabel(obj.app.grid(4),'Text','Population size','Tooltip','Population size of the trainer')); + obj.app.editD(1) = GUI.APP(1,3,uieditfield(obj.app.grid(4),'numeric','Value',50,'Limits',[10 inf],'RoundFractionalValues',true,'Tooltip','Population size of the trainer')); + GUI.APP(1,[5 6],uilabel(obj.app.grid(4),'Text','Max evaluations','FontSize',11,'Tooltip','Maximum number of function evaluations of the trainer')); + obj.app.editD(2) = GUI.APP(1,7,uieditfield(obj.app.grid(4),'numeric','Value',10000,'ValueDisplayFormat','%d','Limits',[100 inf],'RoundFractionalValues',true,'Tooltip','Maximum number of function evaluations of the trainer')); + GUI.APP(2,[1 2],uilabel(obj.app.grid(4),'Text','Execution times','FontSize',11,'Tooltip','Times of using each algorithm to solve the problem in algorithm performance evaluation')); + obj.app.editD(3) = GUI.APP(2,3,uieditfield(obj.app.grid(4),'numeric','Value',3,'Limits',[1 inf],'RoundFractionalValues',true,'Tooltip','Times of using each algorithm to solve the problem in algorithm performance evaluation')); + GUI.APP(2,[5 6],uilabel(obj.app.grid(4),'Text','Parallel execution','FontSize',10,'Tooltip','Perform the training with multiple CPUs')); + obj.app.checkD = GUI.APP(2,7,uicheckbox(obj.app.grid(4),'Text','','Tooltip','Perform the training with multiple CPUs','Enable',~isempty(ver('parallel')))); + GUI.APP(3,1,uilabel(obj.app.grid(4),'Text','File path','Tooltip','The population will be automatically loaded from file before training and saved to file after each iteration')); + obj.app.editD(4) = GUI.APP(3,[3 7],uieditfield(obj.app.grid(4),'Value',fullfile(cd,'Algorithms','Blocks','myAlgorithm.mat'),'Tooltip','The population will be automatically loaded from file before training and saved to file after each iteration')); + obj.app.buttonD(1) = GUI.APP(3,2,uibutton(obj.app.grid(4),'Text','...','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_filepath,obj.app.editD(4)},'Tooltip','The population will be automatically loaded from file before training and saved to file after each iteration')); + obj.app.axesD = GUI.APP(4,[1 7],uiaxes(obj.app.grid(4),'BackgroundColor','w','Box','on','FontName','Times New Roman','FontSize',11)); + axtoolbar(obj.app.axesD); + obj.app.buttonD(2) = GUI.APP(5,3,uibutton(obj.app.grid(4),'push','Text','Start','ButtonpushedFcn',@obj.cb_train)); + obj.app.buttonD(3) = GUI.APP(5,5,uibutton(obj.app.grid(4),'push','Text','Stop','Enable',false,'ButtonpushedFcn',@(~,~)set(obj.app.buttonD(2:3),{'Enable','Text'},{true,'Start';false,'Stop'}))); + obj.app.labelD = GUI.APP(5,[1 2],uilabel(obj.app.grid(4),'Text','0.00%','FontSize',9)); + + % The fifth panel + obj.app.grid(5) = GUI.APP(6,7,uigridlayout(obj.app.maingrid,'RowHeight',{'1x',26},'ColumnWidth',{'1x',20,'1x',5,'1x',20,'1x'},'Padding',[5 5 5 0],'BackgroundColor','w','RowSpacing',5,'ColumnSpacing',5)); + obj.app.axesE = GUI.APP(1,[1 7],uiaxes(obj.app.grid(5),'BackgroundColor','w','Box','on')); + axtoolbar(obj.app.axesE); + obj.app.buttonE(1) = GUI.APP(2,3,uibutton(obj.app.grid(5),'push','Text','Start','ButtonpushedFcn',@obj.cb_test)); + obj.app.buttonE(2) = GUI.APP(2,5,uibutton(obj.app.grid(5),'push','Text','Stop','Enable',false,'ButtonpushedFcn',@(~,~)set(obj.app.buttonE(1:2),{'Enable','Text'},{true,'Start';false,'Stop'}))); + obj.app.labelE = GUI.APP(2,[1 2],uilabel(obj.app.grid(5),'Text','0.00%','FontSize',9)); + obj.app.menuE = uicontext(obj.GUI.app.figure,120); + obj.app.menuE.add(' Save best solutions','',@(~,~)GUI.SavePopulation(obj.GUI.app.figure,obj.data{1}.result{end},1)); + obj.app.menuE.add(' Save all solutions','',@(~,~)GUI.SavePopulation(obj.GUI.app.figure,obj.data{1}.result{end},2)); + obj.app.menuE.flush(); + obj.app.buttonE(3) = GUI.APP(2,7,uibutton(obj.app.grid(5),'push','Text','Save','Enable',false,'ButtonpushedFcn',@(~,~)obj.app.menuE.show())); + + % Initialization + obj.app.canvas.UserData = struct('features',[0 1 0 0 0 0 1 0],'object',[],'lastpos',[],'connectable',false); + + obj.cb_filter([],[],0); + obj.app.listC.Value = 'SOP_F1'; + GUI.UpdateAlgProPara(obj.GUI.app.figure,obj.app.listC,obj.app.listD,'PROBLEM',-1); + obj.app.listD.items(1).edit(3).Value = '10'; + + % Read block list + obj.blockList = {}; + Folders = split(genpath(fullfile(fileparts(mfilename('fullpath')),'..','Algorithms','Blocks')),pathsep); + for i = 1 : length(Folders) - 1 + Files = what(Folders{i}); + Files = Files.m; + for j = 1 : length(Files) + try + f = fopen(Files{j}); + if contains(fgetl(f),'< BLOCK') + Comment = {}; + Parameter = {}; + str = fgetl(f); + while ischar(str) && ~isempty(regexp(str,'^\s*%\s*','once')) + str = regexprep(str,'^\s*%\s*','','once'); + str = regexp(str,'\s*---\s*','split'); + if length(str) < 2 + Comment = [Comment,str]; + else + if length(str) == 2 + str{3} = ''; + end + Parameter = [Parameter;str(1:3)]; + end + str = fgetl(f); + end + obj.blockList = [obj.blockList;Files{j}(1:end-2),strjoin(Comment,' '),{Parameter}]; + end + catch + end + end + end + end + end + methods(Access = private) + %% Add a new block + function cb_addblock(obj,~,~) + HighlightObject(obj.app.canvas); + P = obj.GUI.app.figure.Position; + C = obj.GUI.app.figure.CurrentPoint; + f = uifigure('Name','Add a new block','Position',[P(1)+C(1) P(2)+C(2)-200 300 180],'icon',obj.GUI.icon.logo1,'WindowStyle','modal','Resize','off'); + temp.tip = uilabel(f,'Position',[160 140 120 40],'FontSize',11,'Text','','WordWrap',true); + temp.grid = uigridlayout(uipanel(f,'Position',[10 40 280 100],'BorderType','none'),'ColumnWidth',{'1x','1x'},'Scrollable','on'); + temp.labels = []; + temp.edits = []; + drop = uidropdown(f,'Position',[10 150 140 20],'BackgroundColor','w','Items',obj.blockList(:,1),'ItemsData',1:size(obj.blockList,1),'Value',find(ismember(obj.blockList(:,1),'Block_Population')),'Interruptible','off','BusyAction','cancel','UserData',temp,'ValueChangedFcn',@obj.cb_selectblock); + uibutton(f,'Position',[140 8 70 24],'Text','Add','Interruptible','off','BusyAction','cancel','ButtonpushedFcn',{@obj.cb_createblock,drop}); + uibutton(f,'Position',[220 8 70 24],'Text','Cancel','Interruptible','off','BusyAction','cancel','ButtonpushedFcn',@(h,~)delete(h.Parent)); + obj.cb_selectblock(drop); + end + %% Select a block in the drop-down + function cb_selectblock(obj,h,~) + h.UserData.tip.Text = obj.blockList{h.Value,2}; + delete(h.UserData.labels); + delete(h.UserData.edits); + h.UserData.labels = []; + h.UserData.edits = []; + Parameter = obj.blockList{h.Value,3}; + h.UserData.grid.RowHeight = repmat({22},1,max(1,size(Parameter,1))); + if isempty(Parameter) + h.UserData.labels = GUI.APP(1,[1 2],uilabel(h.UserData.grid,'Text','This block does not contain any parameter.','HorizontalAlignment','center')); + else + for i = 1 : size(Parameter,1) + h.UserData.labels = [h.UserData.labels,GUI.APP(i,1,uilabel(h.UserData.grid,'Text',Parameter{i,1},'Tooltip',Parameter{i,3}))]; + h.UserData.edits = [h.UserData.edits,GUI.APP(i,2,uieditfield(h.UserData.grid,'Value',Parameter{i,2},'Tooltip',Parameter{i,3},'HorizontalAlignment','right'))]; + end + end + end + %% Create a new block + function cb_createblock(obj,h,~,drop) + index = drop.Value; + para = cell(1,size(obj.blockList{index,3},1)); + for i = 1 : length(para) + para{i} = str2num(drop.UserData.edits(i).Value); + end + block = feval(obj.blockList{index,1},para{:}); + app = CreateBlockObject(obj,true); + obj.Graph = addnode(obj.Graph,table(block,app)); + RefreshText(obj.Graph,obj.app.canvas.UserData.features,numnodes(obj.Graph),1); + delete(h.Parent); + end + %% Move on the figure + function cb_figmove(obj,~,~) + h = obj.app.canvas.UserData.object; + if isempty(h.UserData) + % Move a block + h.Children(2).Position(1:2) = h.Children(2).Position(1:2) + obj.app.canvas.CurrentPoint(1,1:2) - obj.app.canvas.UserData.lastpos; + h.Children(1).Position(1:2) = h.Children(2).Position(1:2) + [7 4.5]; + obj.app.canvas.UserData.connectable = false; + blockNo = find(h==obj.Graph.Nodes.app); + % Move the lines starting from the block + for i = outedges(obj.Graph,blockNo)' + obj.Graph.Edges.app(i).UserData = obj.Graph.Edges.app(i).UserData + 0.5*(obj.app.canvas.CurrentPoint(1,1:2)-obj.app.canvas.UserData.lastpos); + FlushCurve(obj.Graph.Edges.app(i),h.Children(1).Position(1:2),[obj.Graph.Edges.app(i).Children(2).XData(end),obj.Graph.Edges.app(i).Children(2).YData(end)]); + end + % Move the lines ending at the block + for i = inedges(obj.Graph,blockNo)' + obj.Graph.Edges.app(i).UserData = obj.Graph.Edges.app(i).UserData + 0.5*(obj.app.canvas.CurrentPoint(1,1:2)-obj.app.canvas.UserData.lastpos); + FlushCurve(obj.Graph.Edges.app(i),[obj.Graph.Edges.app(i).Children(2).XData(1),obj.Graph.Edges.app(i).Children(2).YData(1)],h.Children(1).Position(1:2)); + end + else + % Move a line + h.UserData = h.UserData + 2*(obj.app.canvas.CurrentPoint(1,1:2)-obj.app.canvas.UserData.lastpos); + FlushCurve(h,[h.Children(2).XData(1),h.Children(2).YData(1)],[h.Children(2).XData(end),h.Children(2).YData(end)]); + end + obj.app.canvas.UserData.lastpos = obj.app.canvas.CurrentPoint(1,1:2); + end + %% Click on a block + function cb_block(obj,h,event) + if event.Button ~= 3 + % Left-click on the line + if obj.app.canvas.UserData.connectable && obj.app.canvas.UserData.object ~= h + originNo = find(obj.Graph.Nodes.app==obj.app.canvas.UserData.object); + termiNo = find(obj.Graph.Nodes.app==h); + if findedge(obj.Graph,originNo,termiNo) == 0 + % Create a new line + obj.cb_checkdetail([originNo,termiNo]); + return; + end + end + set(obj.GUI.app.figure,'WindowButtonMotionFcn',@obj.cb_figmove,'WindowButtonUpFcn',@(h,~)set(h,'WindowButtonMotionFcn',@(~,~)[],'WindowButtonUpFcn',[])); + else + % Right-click on the block + set(obj.app.menu.items(3:4),'Enable',true); + obj.app.menu.show(); + end + % Highlight the block + HighlightObject(obj.app.canvas,h); + end + %% Click on a line + function cb_line(obj,h,event) + % Highlight the line + HighlightObject(obj.app.canvas,h); + if event.Button ~= 3 + % Left-click on the line + set(obj.GUI.app.figure,'WindowButtonMotionFcn',@obj.cb_figmove,'WindowButtonUpFcn',@(h,~)set(h,'WindowButtonMotionFcn',@(~,~)[],'WindowButtonUpFcn',[])); + else + % Right-click on the line + set(obj.app.menu.items(3:4),'Enable',false); + obj.app.menu.show(); + end + end + %% Select the features to display + function cb_selectfeature(obj,~,~) + P = obj.GUI.app.figure.Position; + C = obj.GUI.app.figure.CurrentPoint; + f = uifigure('Name','Select features to display','Position',[P(1)+C(1) P(2)+C(2)-200 300 180],'icon',obj.GUI.icon.logo1,'WindowStyle','modal','Resize','off'); + grid = uigridlayout(uipanel(f,'Position',[10 40 280 140],'BorderType','none'),'RowHeight',{20,20,20,20,20,20},'ColumnWidth',{'1x','1x'},'RowSpacing',2); + GUI.APP(1,1,uilabel(grid,'Text','Feature of blocks','FontWeight','bold')); + check(1) = GUI.APP(2,1,uicheckbox(grid,'Text','Block number','Value',obj.app.canvas.UserData.features(1))); + check(2) = GUI.APP(2,2,uicheckbox(grid,'Text','Block name','Value',obj.app.canvas.UserData.features(2))); + check(3) = GUI.APP(3,1,uicheckbox(grid,'Text','#Input solutions','Value',obj.app.canvas.UserData.features(3))); + check(4) = GUI.APP(3,2,uicheckbox(grid,'Text','#Output solutions','Value',obj.app.canvas.UserData.features(4))); + check(5) = GUI.APP(4,1,uicheckbox(grid,'Text','#Parameters','Value',obj.app.canvas.UserData.features(5))); + check(6) = GUI.APP(4,2,uicheckbox(grid,'Text','#Training times','Value',obj.app.canvas.UserData.features(6))); + GUI.APP(5,1,uilabel(grid,'Text','Feature of lines','FontWeight','bold')); + check(7) = GUI.APP(6,1,uicheckbox(grid,'Text','Ratio of solutions','Value',obj.app.canvas.UserData.features(7))); + check(8) = GUI.APP(6,2,uicheckbox(grid,'Text','Number of solutions','Value',obj.app.canvas.UserData.features(8))); + uibutton(f,'Position',[140 8 70 24],'Text','OK','Interruptible','off','BusyAction','cancel','ButtonpushedFcn',{@obj.cb_showfeature,check}); + uibutton(f,'Position',[220 8 70 24],'Text','Cancel','Interruptible','off','BusyAction','cancel','ButtonpushedFcn',@(h,~)delete(h.Parent)); + end + %% Change the features of all blocks and lines + function cb_showfeature(obj,h,~,check) + obj.app.canvas.UserData.features = [check.Value]; + RefreshText(obj.Graph,obj.app.canvas.UserData.features); + delete(h.Parent); + end + %% Arrange the blocks + function cb_arrange(obj,~,~) + tempG = obj.Graph; + tempG = rmedge(tempG,find(ismember(tempG.Edges.EndNodes,tempG.Edges.EndNodes(:,[2,1]),'rows'))); + h = plot(obj.app.canvas,tempG,'Visible',false,'Layout','layered','Direction','right'); + xcoord = (h.XData-min(h.XData)+1e-6)./(max(h.XData)-min(h.XData)+2e-6)*80 + 3; + ycoord = (h.YData-min(h.YData)+1e-6)./(max(h.YData)-min(h.YData)+2e-6)*80 + 6; + delete(h); + axis(obj.app.canvas,[0 100 0 100]); + % Move the blocks + for i = 1 : numnodes(obj.Graph) + obj.Graph.Nodes.app(i).Children(2).Position(1:2) = [xcoord(i),ycoord(i)]; + obj.Graph.Nodes.app(i).Children(1).Position(1:2) = obj.Graph.Nodes.app(i).Children(2).Position(1:2) + [7 4.5]; + end + % Move the lines + for i = 1 : numedges(obj.Graph) + [originNo,termiNo] = findedge(obj.Graph,i); + obj.Graph.Edges.app(i).UserData = (obj.Graph.Nodes.app(originNo).Children(1).Position(1:2)+obj.Graph.Nodes.app(termiNo).Children(1).Position(1:2))/2; + if findedge(obj.Graph,termiNo,originNo) + % Translate the centers of bidirectional edges + obj.Graph.Edges.app(i).UserData(2) = obj.Graph.Edges.app(i).UserData(2) + 16*(-1)^(originNo>termiNo); + end + FlushCurve(obj.Graph.Edges.app(i),obj.Graph.Nodes.app(originNo).Children(1).Position(1:2),obj.Graph.Nodes.app(termiNo).Children(1).Position(1:2)); + end + end + %% Show the details of a block or line + function cb_checkdetail(obj,ui,~) + if nargin > 2 + h = obj.app.canvas.UserData.object; + if isempty(h) + return; + elseif isempty(h.UserData) + blockNo = find(h==obj.Graph.Nodes.app); + else + blockNo = obj.Graph.Edges.EndNodes(h==obj.Graph.Edges.app,:); + end + else + h = []; + blockNo = ui; + end + P = obj.GUI.app.figure.Position; + C = obj.GUI.app.figure.CurrentPoint; + if ~isempty(h) && isempty(h.UserData) + % Show the details of a block + f = uifigure('Name','Details of block','Position',[P(1)+C(1) P(2)+C(2)-260 400 240],'icon',obj.GUI.icon.logo1,'WindowStyle','modal','Resize','off'); + index = find(ismember(obj.blockList(:,1),class(obj.Graph.Nodes.block(blockNo)))); + Parameter = obj.blockList{index,3}; + uilabel(f,'Position',[15 210 370 30],'HorizontalAlignment','center','Text',[GetBlockName(blockNo,obj.Graph.Nodes.block(blockNo),[1 1]),' (',obj.blockList{index,2},')'],'BackgroundColor',h.Children(2).FaceColor,'FontWeight','bold','WordWrap',true); + grid = uigridlayout(uipanel(f,'Position',[10 40 380 170],'BorderType','none'),'RowHeight',[{28,28,18,18},repmat({28},1,size(Parameter,1))],'ColumnWidth',{'5x','5x'},'RowSpacing',2,'Scrollable','on'); + GUI.APP(1,1,uilabel(grid,'Text','Predecessors:')); + GUI.APP(1,2,uilabel(grid,'Text',[strjoin(arrayfun(@(s)GetBlockName(s,obj.Graph.Nodes.block(s),[1 1]),predecessors(obj.Graph,blockNo),'UniformOutput',false),', '),' (',GetInOutNumber(obj.Graph,blockNo,1),' solutions)'],'WordWrap',true)); + GUI.APP(2,1,uilabel(grid,'Text','Successors:')); + GUI.APP(2,2,uilabel(grid,'Text',[strjoin(arrayfun(@(s)GetBlockName(s,obj.Graph.Nodes.block(s),[1 1]),successors(obj.Graph,blockNo),'UniformOutput',false),', '),' (',GetInOutNumber(obj.Graph,blockNo,2),' solutions)'],'WordWrap',true)); + GUI.APP(3,1,uilabel(grid,'Text','Number of parameters:')); + GUI.APP(3,2,uilabel(grid,'Text',num2str(length(obj.Graph.Nodes.block(blockNo).parameter)))); + GUI.APP(4,1,uilabel(grid,'Text','Number of training times:')); + GUI.APP(4,2,uilabel(grid,'Text',num2str(obj.Graph.Nodes.block(blockNo).trainTime))); + for i = 1 : size(Parameter,1) + GUI.APP(4+i,1,uilabel(grid,'Text',['<',Parameter{i,1},'> ',Parameter{i,3}],'WordWrap',true)); + GUI.APP(4+i,2,uilabel(grid,'Text',num2str(obj.Graph.Nodes.block(blockNo).(Parameter{i,1})))); + end + uibutton(f,'Position',[320 8 70 24],'Text','OK','Interruptible','off','BusyAction','cancel','ButtonpushedFcn',@(h,~)delete(h.Parent)); + else + % Show the details of a line + f = uifigure('Name','Details of line','Position',[P(1)+C(1) P(2)+C(2)-150 260 130],'icon',obj.GUI.icon.logo1,'WindowStyle','modal','Resize','off'); + grid = uigridlayout(uipanel(f,'Position',[10 40 240 90],'BorderType','none'),'RowHeight',{22,22,22},'ColumnWidth',{'5x','5x'},'RowSpacing',2,'Scrollable','on'); + GUI.APP(1,1,uilabel(grid,'Text','Origin')); + GUI.APP(1,2,uilabel(grid,'Text',GetBlockName(blockNo(1),obj.Graph.Nodes.block(blockNo(1)),[1 1]),'HorizontalAlignment','right')); + GUI.APP(2,1,uilabel(grid,'Text','Terminus')); + GUI.APP(2,2,uilabel(grid,'Text',GetBlockName(blockNo(2),obj.Graph.Nodes.block(blockNo(2)),[1 1]),'HorizontalAlignment','right')); + GUI.APP(3,1,uilabel(grid,'Text','Ratio of solutions')); + if isempty(h) + edit = GUI.APP(3,2,uieditfield(grid,'numeric','Value',1,'Limits',[0 1])); + else + edit = GUI.APP(3,2,uieditfield(grid,'numeric','Value',obj.Graph.Edges.Weight(h==obj.Graph.Edges.app),'Limits',[0 1])); + end + uibutton(f,'Position',[100 8 70 24],'Text','OK','Interruptible','off','BusyAction','cancel','ButtonpushedFcn',{@obj.cb_createline,blockNo,edit}); + uibutton(f,'Position',[180 8 70 24],'Text','Cancel','Interruptible','off','BusyAction','cancel','ButtonpushedFcn',@(h,~)delete(h.Parent)); + end + end + %% Create a new line + function cb_createline(obj,h,~,blockNo,edit) + lineNo = findedge(obj.Graph,blockNo(1),blockNo(2)); + if lineNo == 0 + if edit.Value > 0 + % Add a line + app = CreateLineObject(obj,blockNo,true); + obj.Graph = addedge(obj.Graph,blockNo(1),blockNo(2),table(edit.Value,app,'VariableNames',{'Weight','app'})); + RefreshText(obj.Graph,obj.app.canvas.UserData.features,find(app==obj.Graph.Edges.app),2); + end + else + if edit.Value > 0 + obj.Graph.Edges.Weight(lineNo) = edit.Value; + RefreshText(obj.Graph,obj.app.canvas.UserData.features,lineNo,2); + else + % Delete an existing line + delete(obj.Graph.Edges.app(lineNo)); + obj.Graph = rmedge(obj.Graph,lineNo); + obj.app.canvas.UserData.object = []; + end + end + delete(h.Parent); + end + %% Change the color of a block or line + function cb_setcolor(obj,~,~) + if ~isempty(obj.app.canvas.UserData.object) + h = obj.app.canvas.UserData.object; + if isempty(h.UserData) + color = uisetcolor(h.Children(2).FaceColor); + if length(color) == 3 + h.Children(2).FaceColor = color; + end + else + color = uisetcolor(h.Children(2).UserData); + if length(color) == 3 + h.Children(2).UserData = color; + HighlightObject(obj.app.canvas); + end + end + figure(obj.GUI.app.figure); + end + end + %% Open the file of a block + function cb_openfile(obj,~,~) + if ~isempty(obj.app.canvas.UserData.object) + blockNo = find(obj.app.canvas.UserData.object==obj.Graph.Nodes.app); + strP = which(class(obj.Graph.Nodes.block(blockNo))); % Name of pcode + web(['file://',strP(1:end-1),'m'],'-browser'); + end + end + %% Delete a block or line + function cb_delete(obj,~,~) + h = obj.app.canvas.UserData.object; + if ~isempty(h) + if isempty(h.UserData) + % Delete a block + blockNo = find(h==obj.Graph.Nodes.app); + delete(h); + if numedges(obj.Graph) > 0 + delete(obj.Graph.Edges.app(outedges(obj.Graph,blockNo))); + delete(obj.Graph.Edges.app(inedges(obj.Graph,blockNo))); + end + obj.Graph = rmnode(obj.Graph,blockNo); + RefreshText(obj.Graph,obj.app.canvas.UserData.features); + else + % Delete a line + lineNo = find(h==obj.Graph.Edges.app); + delete(h); + obj.Graph = rmedge(obj.Graph,lineNo); + end + obj.app.canvas.UserData.object = []; + obj.app.canvas.UserData.connectable = false; + end + end + %% Load a block + function cb_loadblock(obj,~,~) + [Name,Path] = uigetfile({'*.mat','MAT file'}); + figure(obj.GUI.app.figure); + if ischar(Name) + try + data = struct2cell(load(fullfile(Path,Name),'-mat')); + success = false; + for i = 1 : length(data) + if isa(data{i},'BLOCK') + for j = 1 : length(data{i}) + % Add a block object + block = data{i}(j); + app = CreateBlockObject(obj,true); + obj.Graph = addnode(obj.Graph,table(block,app)); + RefreshText(obj.Graph,obj.app.canvas.UserData.features,numnodes(obj.Graph),1); + success = true; + end + end + end + assert(success); + catch + uialert(obj.GUI.app.figure,'The selected file does not contain BLOCK objects.','Error'); + end + end + end + %% Load an algorithm + function cb_loadalgorithm(obj,~,~) + if numnodes(obj.Graph)==0 || strcmp('OK',uiconfirm(obj.GUI.app.figure,'Load a new algorithm? All existing blocks will be deleted.','Warning','Icon','warning')) + [Name,Path] = uigetfile({'*.mat','MAT file'}); + figure(obj.GUI.app.figure); + if ischar(Name) + try + load(fullfile(Path,Name),'Blocks','Graph','-mat'); + SetNewAlgorithm(obj,Blocks,Graph); + catch + uialert(obj.GUI.app.figure,['Fail to load the variables Blocks and Graph from ',fullfile(Path,Name)],'Error'); + end + end + end + end + %% Save a block + function cb_saveblock(obj,~,~) + if ~isempty(obj.app.canvas.UserData.object) + [Name,Path] = uiputfile({'*.mat','MAT file'},'','myBlock'); + figure(obj.GUI.app.figure); + if ischar(Name) + try + block = obj.Graph.Nodes.block(obj.app.canvas.UserData.object==obj.Graph.Nodes.app); + save(fullfile(Path,Name),'block','-mat'); + catch err + uialert(obj.GUI.app.figure,'Fail to save the block, please refer to the command window for details.','Error'); + rethrow(err); + end + end + end + end + %% Save an algorithm + function cb_savealgorithm(obj,~,~,Name,Path) + if nargin < 5 + [Name,Path] = uiputfile({'*.mat','MAT file'},'','myAlgorithm'); + figure(obj.GUI.app.figure); + end + if ischar(Name) + try + Blocks = obj.Graph.Nodes.block'; + Graph = full(adjacency(obj.Graph,'weighted')); + save(fullfile(Path,Name),'Blocks','Graph','-mat'); + catch err + uialert(obj.GUI.app.figure,'Fail to save the algorithm, please refer to the command window for details.','Error'); + rethrow(err); + end + end + end + %% Generate the code of the algorithm + function cb_generatecode(obj,~,~) + if numnodes(obj.Graph) > 0 + [Name,Path] = uiputfile({'*.m','MATLAB function'},'',fullfile(cd,'myAlgorithm')); + figure(obj.GUI.app.figure); + if ischar(Name) + % Save the source code + try + [~,name] = fileparts(Name); + Code = {['classdef ',name,' < BlockEA % < ALGORITHM'] + '' + ' methods' + [' function obj = ',name,'(varargin)'] + ' obj = obj@BlockEA(varargin{:});'}; + for i = 1 : numnodes(obj.Graph) + Parameter = obj.blockList{ismember(obj.blockList(:,1),class(obj.Graph.Nodes.block(i))),3}; + paras = cell(1,size(Parameter,1)); + for j = 1 : size(Parameter,1) + paras{j} = num2str(obj.Graph.Nodes.block(i).(Parameter{j,1})); + end + if i == 1 + str = ' Blocks = ['; + else + str = ' '; + end + str = [str,class(obj.Graph.Nodes.block(i)),'(',strjoin(paras,','),')']; + if i < numnodes(obj.Graph) + str = [str,',...']; + else + str = [str,'];']; + end + Code = [Code;str]; + end + adjMat = num2str(full(adjacency(obj.Graph,'weighted'))); + for i = 1 : size(adjMat,1) + if i == 1 + str = ' Graph = ['; + else + str = ' '; + end + str = [str,adjMat(i,:)]; + if i >= size(adjMat,1) + str = [str,'];']; + end + Code = [Code;str]; + end + Code = [Code; + {''} + [' load(''',name,'.mat'',''Blocks'',''Graph'');'] + {''} + ' obj.parameter = {Blocks,Graph};' + ' end' + ' end' + 'end']; + fid = fopen(fullfile(Path,Name),'wt'); + for i = 1 : length(Code) + fprintf(fid,'%s\n',Code{i}); + end + fclose(fid); + web(['file://',fullfile(Path,Name)],'-browser'); + catch err + uialert(obj.GUI.app.figure,'Fail to generate code, please refer to the command window for details.','Error'); + rethrow(err); + end + % Save the algorithm + obj.cb_savealgorithm([],[],[name,'.mat'],Path); + end + end + end + %% Valid the algorithm + function cb_validation(obj,~,~) + try + % Generate the ALGORITHM object + Blocks = obj.Graph.Nodes.block; + Graph = adjacency(obj.Graph,'weighted'); + Blocks.Validity(Graph); + ALG = BlockEA('parameter',{Blocks,Graph},'outputFcn',@(~,~)[]); + % Generate the PROBLEM object + [name,para] = GUI.GetParameterSetting(obj.app.listD.items(1)); + PRO = feval(name,'N',para{1},'M',para{2},'D',para{3},'maxFE',para{1}+1,'parameter',para(5:end)); + % Execute the algorithm for one generation + ALG.Solve(PRO); + RefreshText(obj.Graph,obj.app.canvas.UserData.features); + uialert(obj.GUI.app.figure,'The algorithm is valid.','Valid algorithm','Icon','success'); + catch err + err = addCause(err,MException('','The algorithm is invalid')); + uialert(obj.GUI.app.figure,sprintf('%s, since %s',err.cause{end}.message,err.message),'Invalid algorithm'); + rethrow(err); + end + end + %% Select a predefined algorithm + function cb_selectAlgorithm(obj,h,~) + if h.Value == 1 + if ~isempty(h.UserData) + SetNewAlgorithm(obj); + obj.Graph = h.UserData; + if numnodes(obj.Graph) > 0 + set(obj.Graph.Nodes.app,'Visible',true); + end + if numedges(obj.Graph) > 0 + set(obj.Graph.Edges.app,'Visible',true); + end + RefreshText(obj.Graph,obj.app.canvas.UserData.features); + h.UserData = []; + end + else + switch h.Value + case 2 + Blocks = [Block_Population(),Block_Tournament(200,5),Block_Crossover(2,5),Block_Mutation(5),Block_Selection(100)]; + Graph = [0 1 0 0 1 + 0 0 1 0 0 + 0 0 0 1 0 + 0 0 0 0 1 + 1 0 0 0 0]; + case 3 + Blocks = [Block_Population(),Block_Tournament(100,5),Block_Crossover(2,5),Block_Mutation(5),Block_Selection(100),... + Block_Population(),Block_Tournament(100,5),Block_Crossover(2,5),Block_Mutation(5),Block_Selection(100)]; + Graph = [0 1 0 0 1 0 0 0 0 0 + 0 0 1 0 0 0 0 0 0 0 + 0 0 0 1 0 0 0 0 0 0 + 0 0 0 0 1 0 0 0 0 1 + 1 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 1 0 0 1 + 0 0 0 0 0 0 0 1 0 0 + 0 0 0 0 0 0 0 0 1 0 + 0 0 0 0 1 0 0 0 0 1 + 0 0 0 0 0 1 0 0 0 0]; + case 4 + Blocks = [Block_Population(),Block_Tournament(4,10),Block_Tournament(4,10),Block_Tournament(4,10),... + Block_Exchange(3),Block_Exchange(3),Block_Exchange(3),Block_Exchange(3),... + Block_Crossover(2,5),Block_Selection(100)]; + Graph = [0 1 1 1 0 0 0 0 0 1 + 0 0 0 0 1/4 1/4 1/4 1/4 0 0 + 0 0 0 0 1/4 1/4 1/4 1/4 0 0 + 0 0 0 0 1/4 1/4 1/4 1/4 0 0 + 0 0 0 0 0 0 0 0 1 0 + 0 0 0 0 0 0 0 0 1 0 + 0 0 0 0 0 0 0 0 1 0 + 0 0 0 0 0 0 0 0 1 0 + 0 0 0 0 0 0 0 0 0 1 + 1 0 0 0 0 0 0 0 0 0]; + case 5 + Blocks = [Block_Population(),Block_Tournament(4,10),Block_Tournament(4,10),Block_Tournament(4,10),... + Block_Exchange(3),Block_Exchange(3),Block_Exchange(3),Block_Exchange(3),... + Block_Crossover(2,5),Block_Mutation(5),Block_Selection(100)]; + Graph = [0 1 1 1 0 0 0 0 0 0 1 + 0 0 0 0 1/4 1/4 1/4 1/4 0 0 0 + 0 0 0 0 1/4 1/4 1/4 1/4 0 0 0 + 0 0 0 0 1/4 1/4 1/4 1/4 0 0 0 + 0 0 0 0 0 0 0 0 1 0 0 + 0 0 0 0 0 0 0 0 1 0 0 + 0 0 0 0 0 0 0 0 1 0 0 + 0 0 0 0 0 0 0 0 1 0 0 + 0 0 0 0 0 0 0 0 0 1 0 + 0 0 0 0 0 0 0 0 0 0 1 + 1 0 0 0 0 0 0 0 0 0 0]; + end + if isempty(h.UserData) + if numnodes(obj.Graph) > 0 + set(obj.Graph.Nodes.app,'Visible',false); + end + if numedges(obj.Graph) > 0 + set(obj.Graph.Edges.app,'Visible',false); + end + h.UserData = obj.Graph; + obj.Graph = digraph(); + end + SetNewAlgorithm(obj,Blocks,Graph); + end + end + %% Update the problems in the list + function cb_filter(obj,~,~,index) + GUI.UpdateAlgProList(index,obj.app.stateC,obj.app.listC,obj.app.labelC(5),obj.GUI.proList); + end + %% Determine the file to load and save training results + function cb_filepath(obj,~,~,edit) + [Name,Path] = uigetfile({'*.mat','MAT file'},'',fileparts(edit.Value)); + figure(obj.GUI.app.figure); + if ischar(Name) + edit.Value = fullfile(Path,Name); + end + end + %% Start the testing + function cb_test(obj,~,~) + if strcmp(obj.app.buttonE(1).Text,'Pause') + obj.app.buttonE(1).Text = 'Continue'; + elseif strcmp(obj.app.buttonE(1).Text,'Continue') + obj.app.buttonE(1).Text = 'Pause'; + else + % Generate the ALGORITHM object + try + Blocks = obj.Graph.Nodes.block; + Graph = adjacency(obj.Graph,'weighted'); + Blocks.Validity(Graph); + ALG = BlockEA('parameter',{Blocks,Graph},'outputFcn',@obj.outputFcnTest,'save',1); + catch err + err = addCause(err,MException('','The algorithm is invalid')); + uialert(obj.GUI.app.figure,sprintf('%s, since %s',err.cause{end}.message,err.message),'Invalid algorithm'); + rethrow(err); + end + % Generate the PROBLEM object + try + [name,para] = GUI.GetParameterSetting(obj.app.listD.items(1)); + PRO = feval(name,'N',para{1},'M',para{2},'D',para{3},obj.app.listD.items(1).label(4).Text,para{4},'parameter',para(5:end)); + catch err + uialert(obj.GUI.app.figure,err.message,'Invalid parameter settings'); + return; + end + % Update the data + obj.data = {ALG,PRO}; + % Update the GUI + set([obj.GUI.app.button,obj.app.buttonA,obj.app.dropA,obj.app.stateC,obj.app.listC,obj.app.editD,obj.app.checkD,obj.app.buttonD],'Enable',false); + obj.app.listD.Enable = false; + obj.app.buttonE(1).Text = 'Pause'; + set(obj.app.buttonE(2:3),'Enable',true); + delete(obj.app.axesE.Children); + % Execute the algorithm + try + ALG.Solve(PRO); + obj.cb_stoptest(); + catch err + uialert(obj.GUI.app.figure,'The algorithm terminates unexpectedly, please refer to the command window for details.','Error'); + obj.cb_stoptest(); + rethrow(err); + end + end + end + %% Stop the testing + function cb_stoptest(obj,~,~,metValue) + if nargin < 4 + RefreshText(obj.Graph,obj.app.canvas.UserData.features); + set([obj.GUI.app.button,obj.app.buttonA,obj.app.dropA,obj.app.stateC,obj.app.listC,obj.app.editD,obj.app.buttonD(1:2)],'Enable',true); + obj.app.checkD.Enable = ~isempty(ver('parallel')); + obj.app.listD.Enable = true; + obj.app.buttonE(1).Text = 'Start'; + obj.app.buttonE(2).Enable = false; + obj.app.buttonE(3).Enable = ~isempty(obj.data{1}.result); + end + if isempty(obj.data{1}.result) + obj.data = {}; + else + if nargin < 4 + if obj.data{2}.M == 1 + metValue = obj.data{2}.CalMetric('Min_value',obj.data{1}.result{end}); + else + metValue = obj.data{2}.CalMetric('HV',obj.data{1}.result{end}); + end + end + Draw(obj.app.axesE); + if obj.data{2}.M == 1 + obj.data{2}.DrawDec(obj.data{1}.result{end}); + else + obj.data{2}.DrawObj(obj.data{1}.result{end}); + end + obj.app.axesE.FontSize = 11; + if obj.data{2}.M == 1 + obj.app.labelE.Text = sprintf('Min value: %.2e',metValue); + else + obj.app.labelE.Text = sprintf('HV: %.2e',metValue); + end + end + end + %% Start the training + function cb_train(obj,~,~) + if strcmp(obj.app.buttonD(2).Text,'Pause') + obj.app.buttonD(2).Text = 'Continue'; + elseif strcmp(obj.app.buttonD(2).Text,'Continue') + obj.app.buttonD(2).Text = 'Pause'; + else + % Generate the ALGORITHM object of algorithm + try + Blocks = obj.Graph.Nodes.block; + Graph = adjacency(obj.Graph,'weighted'); + Blocks.Validity(Graph); + obj.data{1} = BlockEA('parameter',{Blocks,Graph},'outputFcn',@(~,~)[],'save',1); + catch err + err = addCause(err,MException('','The algorithm is invalid')); + uialert(obj.GUI.app.figure,sprintf('%s, since %s',err.cause{end}.message,err.message),'Invalid algorithm'); + rethrow(err); + end + % Generate the PROBLEM object of algorithm + try + [name,para] = GUI.GetParameterSetting(obj.app.listD.items(1)); + obj.data{2} = feval(name,'N',para{1},'M',para{2},'D',para{3},obj.app.listD.items(1).label(4).Text,para{4},'parameter',para(5:end)); + catch err + uialert(obj.GUI.app.figure,err.message,'Invalid parameter settings'); + return; + end + % Update the GUI + set([obj.GUI.app.button,obj.app.buttonA,obj.app.dropA,obj.app.stateC,obj.app.listC,obj.app.editD,obj.app.checkD,obj.app.buttonD(1),obj.app.buttonE],'Enable',false); + obj.app.listD.Enable = false; + obj.app.buttonD(2).Text = 'Pause'; + obj.app.buttonD(3).Enable = true; + delete(obj.app.axesD.Children); + delete(obj.app.axesE.Children); + try + % Generate the ALGORITHM object of trainer + obj.dataTrain{1} = GA('outputFcn',@obj.outputFcnTrain,'save',1); + % Generate the PROBLEM object of trainer + obj.dataTrain{2} = UserProblem('N',obj.app.editD(1).Value,'maxFE',obj.app.editD(2).Value,'D',length(Blocks.parameters),'lower',Blocks.lowers,'upper',Blocks.uppers,'initFcn',@obj.trainInit,'objFcn',@obj.trainObj,'once',true); + % Execute the training + obj.dataTrain{1}.Solve(obj.dataTrain{2}); + obj.cb_stoptrain(); + catch err + obj.cb_stoptrain(); + if ~strcmp(err.identifier,'PlatEMO:Termination') + uialert(obj.GUI.app.figure,'The trainer terminates unexpectedly, please refer to the command window for details.','Error'); + rethrow(err); + end + end + end + end + %% Stop the training + function cb_stoptrain(obj,~,~) + RefreshText(obj.Graph,obj.app.canvas.UserData.features); + set([obj.GUI.app.button,obj.app.buttonA,obj.app.dropA,obj.app.stateC,obj.app.listC,obj.app.editD,obj.app.buttonD(1),obj.app.buttonE(1:2)],'Enable',true); + obj.app.checkD.Enable = ~isempty(ver('parallel')); + obj.app.listD.Enable = true; + obj.app.buttonD(2).Text = 'Start'; + obj.app.buttonD(3).Enable = false; + obj.app.buttonE(3).Enable = ~isempty(obj.data{1}.result); + if isempty(obj.data{1}.result) + obj.data = {}; + end + if isempty(obj.dataTrain{1}.result) + obj.dataTrain = {}; + else + if obj.data{2}.M == 1 + obj.app.labelD.Text = sprintf('Min value: %.2e',min(obj.dataTrain{1}.result{end}.objs)); + else + obj.app.labelD.Text = sprintf('HV: %.2e',-min(obj.dataTrain{1}.result{end}.objs)); + end + end + end + %% Output function of single test + function outputFcnTest(obj,Algorithm,Problem) + assert(strcmp(obj.app.buttonE(2).Enable,'on'),'PlatEMO:Termination',''); + obj.app.labelE.Text = sprintf('%.2f%%',Problem.FE/Problem.maxFE*100); + if strcmp(obj.app.buttonE(1).Text,'Continue') + waitfor(obj.app.buttonE(1),'Text'); + end + assert(strcmp(obj.app.buttonE(2).Enable,'on'),'PlatEMO:Termination',''); + end + %% Output function of training + function outputFcnTrain(obj,Algorithm,Problem) + assert(strcmp(obj.app.buttonD(3).Enable,'on'),'PlatEMO:Termination',''); + % Show the results + obj.app.labelD.Text = sprintf('%.2f%%',Problem.FE/Problem.maxFE*100); + if Problem.M == 1 + point = [Algorithm.result{end,1},min(Algorithm.result{end,2}.objs)]; + else + point = [Algorithm.result{end,1},-min(Algorithm.result{end,2}.objs)]; + end + if isempty(obj.app.axesD.Children) + plot(obj.app.axesD,point(1),point(2),'-k.','LineWidth',1); + obj.app.axesD.YScale = 'linear'; + else + [obj.app.axesD.Children(1).XData,obj.app.axesD.Children(1).YData] = deal([obj.app.axesD.Children(1).XData,point(1)],[obj.app.axesD.Children(1).YData,point(2)]); + end + if min(obj.app.axesD.Children(1).YData) > 0 && max(obj.app.axesD.Children(1).YData) > 10*min(obj.app.axesD.Children(1).YData) + obj.app.axesD.YScale = 'log'; + end + % Save the results + try + Population = Algorithm.result{end}; + [~,best] = min(Population.objs); + obj.Graph.Nodes.block.ParameterSet(Population(best).dec); + Blocks = obj.Graph.Nodes.block'; + Graph = full(adjacency(obj.Graph,'weighted')); + save(obj.app.editD(4).Value,'Blocks','Graph','Population','-mat'); + catch err + uialert(obj.GUI.app.figure,'Fail to save the population, please refer to the command window for details.','Error'); + rethrow(err); + end + if strcmp(obj.app.buttonD(2).Text,'Continue') + waitfor(obj.app.buttonD(2),'Text'); + end + assert(strcmp(obj.app.buttonD(3).Enable,'on'),'PlatEMO:Termination',''); + end + %% Initialization function for training + function PopDec = trainInit(obj,N) + D = length(obj.Graph.Nodes.block.parameters); + if exist(obj.app.editD(4).Value,'file') + try + load(obj.app.editD(4).Value,'-mat','Population'); + PopDec = Population.decs; + catch + error(['Fail to load the variable Population from ',obj.app.editD(4).Value]); + end + assert(D==size(PopDec,2),'The current algorithm has %d parameters while the loaded Population has %d variables',D,size(PopDec,2)); + else + PopDec = []; + end + if size(PopDec,1) > N + PopDec = PopDec(1:N,:); + elseif size(PopDec,1) < N + PopDec = [PopDec;unifrnd(repmat(obj.Graph.Nodes.block.lowers,N-size(PopDec,1),1),repmat(obj.Graph.Nodes.block.uppers,N-size(PopDec,1),1))]; + end + end + %% Objective function for training + function PopObj = trainObj(obj,PopDec) + PopObj = zeros(size(PopDec,1),obj.app.editD(3).Value); + if ~obj.app.checkD.Value % Evaluation in sequence + for i = 1 : size(PopObj,1) + obj.data{1}.parameter{1}.ParameterSet(PopDec(i,:)); + for j = 1 : size(PopObj,2) + assert(strcmp(obj.app.buttonD(3).Enable,'on'),'PlatEMO:Termination',''); + obj.data{1}.Solve(obj.data{2}); + if obj.data{2}.M == 1 + PopObj(i,j) = obj.data{2}.CalMetric('Min_value',obj.data{1}.result{end}); + obj.cb_stoptest([],[],PopObj(i,j)); + else + PopObj(i,j) = -obj.data{2}.CalMetric('HV',obj.data{1}.result{end}); + obj.cb_stoptest([],[],-PopObj(i,j)); + end + if strcmp(obj.app.buttonD(2).Text,'Continue') + waitfor(obj.app.buttonD(2),'Text'); + end + assert(strcmp(obj.app.buttonD(3).Enable,'on'),'PlatEMO:Termination',''); + end + end + else % Evaluation in parallel + for b = obj.Graph.Nodes.block' + b.trainTime = b.trainTime + size(PopDec,1); + end + for i = 1 : numel(PopObj) + Future(i) = parfeval(@parallelFcn,1,obj.data{1},obj.data{2},PopDec(mod(i-1,size(PopDec,1))+1,:)); + end + while ~all([Future.Read]) + drawnow('limitrate'); + if strcmp(obj.app.buttonD(3).Enable,'off') + cancel(Future); + error('PlatEMO:Termination',''); + end + [r,ALG] = fetchNext(Future,0.01); + if ~isempty(r) + obj.data{1} = ALG; + [i,j] = ind2sub(size(PopObj),r); + if obj.data{2}.M == 1 + PopObj(i,j) = obj.data{2}.CalMetric('Min_value',obj.data{1}.result{end}); + obj.cb_stoptest([],[],PopObj(i,j)); + else + PopObj(i,j) = -obj.data{2}.CalMetric('HV',obj.data{1}.result{end}); + obj.cb_stoptest([],[],-PopObj(i,j)); + end + end + if strcmp(obj.app.buttonD(2).Text,'Continue') + waitfor(obj.app.buttonD(2),'Text'); + end + if strcmp(obj.app.buttonD(3).Enable,'off') + cancel(Future); + error('PlatEMO:Termination',''); + end + end + end + PopObj = max(PopObj,[],2); end end +end + +%% Create the graphic objects of a block +function app = CreateBlockObject(obj,highlight) + app = hggroup(obj.app.canvas,'ButtonDownFcn',@obj.cb_block); + rectangle(app,'Position',[0 91 14 9],'FaceColor',[.95 .95 1],'EdgeColor',[.8 .8 .8],'Curvature',[0.1 0.1],'HitTest',false); + text(app,7,95.5,'','FontSize',11,'HorizontalAlignment','center','Clipping',true,'HitTest',false); + if highlight + HighlightObject(obj.app.canvas,app); + else + HighlightObject(obj.app.canvas); + end +end + +%% Create the graphic objects of a line +function app = CreateLineObject(obj,blockNo,highlight) + origin = obj.Graph.Nodes.app(blockNo(1)).Children(1).Position; + termi = obj.Graph.Nodes.app(blockNo(2)).Children(1).Position; + app = hggroup(obj.app.canvas,'ButtonDownFcn',@obj.cb_line,'UserData',(origin(1:2)+termi(1:2))/2); + line(app,0,0,'Color','k','UserData',[0 0 0]); + text(app,0,0,'>','Margin',0.1,'Color','k','BackgroundColor','w','FontSize',11,'HorizontalAlignment','center','Clipping',true,'HitTest',false); + FlushCurve(app,origin(1:2),termi(1:2)); + obj.app.canvas.Children = obj.app.canvas.Children([2:end,1]); + if highlight + HighlightObject(obj.app.canvas,app); + else + HighlightObject(obj.app.canvas); + end +end + +%% Highlight a block or line in the canvas +function HighlightObject(canvas,app) + if nargin > 1 + HighlightObject(canvas); + app.Children(1).Color = 'b'; + if isempty(app.UserData) + % Highlight a hggroup of block + app.Children(2).EdgeColor = 'b'; + canvas.UserData.connectable = true; + else + % Highlight a hggroup of line + app.Children(2).Color = 'b'; + end + canvas.UserData.object = app; + canvas.UserData.lastpos = canvas.CurrentPoint(1,1:2); + elseif ~isempty(canvas.UserData.object) + if isvalid(canvas.UserData.object) + if isempty(canvas.UserData.object.UserData) + % Unhighlight a hggroup of block + canvas.UserData.object.Children(1).Color = 'k'; + canvas.UserData.object.Children(2).EdgeColor = [.8 .8 .8]; + else + % Unhighlight a hggroup of line + set(canvas.UserData.object.Children,'Color',canvas.UserData.object.Children(2).UserData) + end + end + canvas.UserData.object = []; + canvas.UserData.connectable = false; + end +end + +%% Refresh a Bezier curve +function FlushCurve(curve,origin,termi) + mid = curve.UserData; + t = linspace(0,1,max(20,norm(termi-origin)/2))'; + P = (1-t).^2.*origin + 2*t.*(1-t).*mid + t.^2.*termi; + if P(1,1) < P(end,1) && curve.Children(1).String(1)=='<' + curve.Children(1).String = [curve.Children(1).String(2:end),'>']; + elseif P(1,1) >= P(end,1) && curve.Children(1).String(end)=='>' + curve.Children(1).String = ['<',curve.Children(1).String(1:end-1)]; + end + curve.Children(2).XData = P(:,1); + curve.Children(2).YData = P(:,2); + curve.Children(1).Rotation = atan((P(ceil(end/2)+1,2)-P(ceil(end/2),2))./(P(ceil(end/2)+1,1)-P(ceil(end/2),1)-1e-6).*curve.Parent.Position(4)./curve.Parent.Position(3))/pi*180; + curve.Children(1).Position(1:2) = P(ceil(end/2),:); +end + +%% Refresh the text of a block or line +function RefreshText(Graph,features,no,type) + if nargin < 3 + % Refresh the texts of all blocks and lines + for i = 1 : numnodes(Graph) + RefreshText(Graph,features,i,1); + end + for i = 1 : numedges(Graph) + RefreshText(Graph,features,i,2); + end + elseif type == 1 + % Refresh the text of a block + app = Graph.Nodes.app(no); + block = Graph.Nodes.block(no); + str = {}; + str = [str,GetBlockName(no,block,features(1:2))]; + if features(3) && features(4) + str = [str,['In: ',GetInOutNumber(Graph,no,1),' Out: ',GetInOutNumber(Graph,no,2)]]; + elseif features(3) && ~features(4) + str = [str,['In: ',GetInOutNumber(Graph,no,1)]]; + elseif ~features(3) && features(4) + str = [str,['Out: ',GetInOutNumber(Graph,no,2)]]; + end + if features(5) && features(6) + str = [str,['Para: ',num2str(length(block.parameter)),' Train: ',num2str(block.trainTime)]]; + elseif features(5) && ~features(6) + str = [str,['Para: ',num2str(length(block.parameter))]]; + elseif ~features(5) && features(6) + str = [str,['Train: ',num2str(block.trainTime)]]; + end + app.Children(1).String = str; + elseif type == 2 + % Refresh the text of a line + app = Graph.Edges.app(no); + if features(7) && features(8) + str = [' ',num2str(Graph.Edges.Weight(no),3),'(',GetInOutNumber(Graph,no,3),') ']; + elseif features(7) && ~features(8) + str = [' ',num2str(Graph.Edges.Weight(no),3),' ']; + elseif ~features(7) && features(8) + str = [' (',GetInOutNumber(Graph,no,3),') ']; + else + str = []; + end + if app.Children(2).XData(1) < app.Children(2).XData(end) + str = [str,'>']; + else + str = ['<',str]; + end + app.Children(1).String = str; + end +end + +%% Get the name of a block +function name = GetBlockName(no,block,type) + str = class(block); + if type(1) && type(2) + name = ['#',num2str(no),' ',str(7:end)]; + elseif type(1) && ~type(2) + name = ['#',num2str(no)]; + elseif ~type(1) && type(2) + name = str(7:end); + else + name = []; + end +end + +%% Get the string of number of solutions of a block or line +function num = GetInOutNumber(Graph,no,type) + function len = GetLen(out) + if isa(out,'SOLUTION') + len = length(out); + else + len = size(out,1); + end + end + if type == 1 + % Get the number of input solutions of a block + if no <= numnodes(Graph) + [eid,nid] = inedges(Graph,no); + num = sum(arrayfun(@(e,n)floor(GetLen(Graph.Nodes.block(n).output)*Graph.Edges.Weight(e)),eid,nid)); + else + num = 0; + end + elseif type == 2 + % Get the number of output solutions of a block + num = GetLen(Graph.Nodes.block(no).output); + elseif type == 3 + % Get the number of passed solutions of a line + num = floor(GetLen(Graph.Nodes.block(Graph.Edges.EndNodes(no,1)).output)*Graph.Edges.Weight(no)); + end + num = num2str(num); +end + +%% Delete the current algorithm and set a new algorithm +function SetNewAlgorithm(obj,Blocks,Graph) + if numnodes(obj.Graph) > 0 + delete(obj.Graph.Nodes.app); + end + if numedges(obj.Graph) > 0 + delete(obj.Graph.Edges.app); + end + if nargin > 1 + obj.Graph = digraph(Graph); + obj.Graph.Nodes.block = Blocks(:); + for i = 1 : numnodes(obj.Graph) + obj.Graph.Nodes.app(i) = CreateBlockObject(obj,false); + end + for i = 1 : numedges(obj.Graph) + obj.Graph.Edges.app(i) = CreateLineObject(obj,obj.Graph.Edges.EndNodes(i,:),false); + end + RefreshText(obj.Graph,obj.app.canvas.UserData.features); + obj.cb_arrange(); + end +end + +%% Function for parallelization of algorithm evaluation in training +function Algorithm = parallelFcn(Algorithm,Problem,Dec) + Algorithm.parameter{1}.ParameterSet(Dec); + Algorithm.Solve(Problem); end \ No newline at end of file diff --git a/PlatEMO/GUI/module_exp.m b/PlatEMO/GUI/module_exp.m index 571ff0df2..17f011dbd 100644 --- a/PlatEMO/GUI/module_exp.m +++ b/PlatEMO/GUI/module_exp.m @@ -29,44 +29,27 @@ % The first panel obj.app.grid(1) = GUI.APP(2,1,uigridlayout(obj.app.maingrid,'RowHeight',{16,19,16,19,19,16,19,19,19,20,'1x',20,'1x',21,21,21},'ColumnWidth',{'1x','1x','1x'},'Padding',[8 5 8 0],'RowSpacing',3,'ColumnSpacing',5,'BackgroundColor','w')); - obj.app.labelA(1) = GUI.APP(1,[1 3],uilabel(obj.app.grid(1),'Text','Number of objectives','VerticalAlignment','bottom','FontSize',12,'FontColor',[.15 .6 .2],'FontWeight','bold')); - obj.app.stateA(1) = GUI.APP(2,1,uibutton(obj.app.grid(1),'state','Text','single','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The problem has a single objective','ValueChangedFcn',{@obj.cb_filter,1})); - obj.app.stateA(2) = GUI.APP(2,2,uibutton(obj.app.grid(1),'state','Text','multi','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',1,'Tooltip','The problem has 2 or 3 objectives','ValueChangedFcn',{@obj.cb_filter,2})); - obj.app.stateA(3) = GUI.APP(2,3,uibutton(obj.app.grid(1),'state','Text','many','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The problem has more than 3 objectives','ValueChangedFcn',{@obj.cb_filter,3})); - obj.app.labelA(2) = GUI.APP(3,[1 3],uilabel(obj.app.grid(1),'Text','Encoding scheme','VerticalAlignment','bottom','FontSize',12,'FontColor',[.15 .6 .2],'FontWeight','bold')); - obj.app.stateA(4) = GUI.APP(4,1,uibutton(obj.app.grid(1),'state','Text','real','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',1,'Tooltip','The decision variables are real numbers','ValueChangedFcn',{@obj.cb_filter,4})); - obj.app.stateA(5) = GUI.APP(4,2,uibutton(obj.app.grid(1),'state','Text','integer','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The decision variables are integers','ValueChangedFcn',{@obj.cb_filter,5})); - obj.app.stateA(6) = GUI.APP(4,3,uibutton(obj.app.grid(1),'state','Text','label','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The decision variables are labels','ValueChangedFcn',{@obj.cb_filter,6})); - obj.app.stateA(7) = GUI.APP(5,1,uibutton(obj.app.grid(1),'state','Text','binary','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The decision variables are binary numbers','ValueChangedFcn',{@obj.cb_filter,7})); - obj.app.stateA(8) = GUI.APP(5,2,uibutton(obj.app.grid(1),'state','Text','permutation','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The decision vector is a permutation','ValueChangedFcn',{@obj.cb_filter,8})); - obj.app.labelA(3) = GUI.APP(6,[1 3],uilabel(obj.app.grid(1),'Text','Special difficulties','VerticalAlignment','bottom','FontSize',12,'FontColor',[.15 .6 .2],'FontWeight','bold')); - obj.app.stateA(9) = GUI.APP(7,1,uibutton(obj.app.grid(1),'state','Text','large','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The problem has more than 100 decision variables','ValueChangedFcn',{@obj.cb_filter,9})); - obj.app.stateA(10) = GUI.APP(7,2,uibutton(obj.app.grid(1),'state','Text','constrained','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The problem has constraints','ValueChangedFcn',{@obj.cb_filter,10})); - obj.app.stateA(11) = GUI.APP(7,3,uibutton(obj.app.grid(1),'state','Text','expensive','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The objectives are computationally time-consuming','ValueChangedFcn',{@obj.cb_filter,11})); - obj.app.stateA(12) = GUI.APP(8,1,uibutton(obj.app.grid(1),'state','Text','multimodal','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The objectives are multimodal','ValueChangedFcn',{@obj.cb_filter,12})); - obj.app.stateA(13) = GUI.APP(8,2,uibutton(obj.app.grid(1),'state','Text','sparse','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','Most decision variables of the optimal solutions are zero','ValueChangedFcn',{@obj.cb_filter,13})); - obj.app.stateA(14) = GUI.APP(8,3,uibutton(obj.app.grid(1),'state','Text','dynamic','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The objectives vary periodically','ValueChangedFcn',{@obj.cb_filter,14})); - obj.app.stateA(15) = GUI.APP(9,1,uibutton(obj.app.grid(1),'state','Text','multitask','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The problem has multiple tasks to be solved simultaneously','ValueChangedFcn',{@obj.cb_filter,15})); - obj.app.stateA(16) = GUI.APP(9,2,uibutton(obj.app.grid(1),'state','Text','bilevel','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The problem has two nested objectives','ValueChangedFcn',{@obj.cb_filter,16})); - obj.app.stateA(17) = GUI.APP(9,3,uibutton(obj.app.grid(1),'state','Text','robust','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The objectives are influenced by uncertain factors','ValueChangedFcn',{@obj.cb_filter,17})); + [obj.app.stateA,obj.app.labelA] = GUI.GenerateLabelButton(obj.app.grid(1),[0 1 0 1,zeros(1,13)],@obj.cb_filter); obj.app.labelA(4) = GUI.APP(10,[1 2],uilabel(obj.app.grid(1),'Text','Algorithms','VerticalAlignment','bottom','FontSize',13,'FontColor',[.2 .4 .7],'FontWeight','bold')); obj.app.labelA(5) = GUI.APP(10,3,uilabel(obj.app.grid(1),'HorizontalAlignment','right','VerticalAlignment','bottom','FontSize',10,'FontColor',[.2 .4 .7])); - obj.app.listA(1) = GUI.APP(11,[1 3],uilistbox(obj.app.grid(1),'FontColor',[.2 .4 .7],'ValueChangedFcn',{@obj.cb_updateList,2})); + obj.app.listA(1) = GUI.APP(11,[1 3],uilistbox(obj.app.grid(1),'FontColor',[.2 .4 .7])); obj.app.labelA(6) = GUI.APP(12,[1 2],uilabel(obj.app.grid(1),'Text','Problems','VerticalAlignment','bottom','FontSize',13,'FontColor',[.9 .5 .2],'FontWeight','bold')); obj.app.labelA(7) = GUI.APP(12,3,uilabel(obj.app.grid(1),'HorizontalAlignment','right','VerticalAlignment','bottom','FontSize',10,'FontColor',[.9 .5 .2])); - obj.app.listA(2) = GUI.APP(13,[1 3],uilistbox(obj.app.grid(1),'FontColor',[.9 .5 .2],'ValueChangedFcn',{@obj.cb_updateList,-2})); + obj.app.listA(2) = GUI.APP(13,[1 3],uilistbox(obj.app.grid(1),'FontColor',[.9 .5 .2])); obj.app.labelA(8) = GUI.APP(14,[1 2],uilabel(obj.app.grid(1),'Text','Number of runs','FontColor',[.15 .6 .2],'FontWeight','bold','Tooltip','Number of runs for each algorithm on each problem')); obj.app.editA(1) = GUI.APP(14,3,uieditfield(obj.app.grid(1),'numeric','Value',30,'limits',[1 inf],'RoundFractionalValues','on','Tooltip','Number of runs for each algorithm on each problem')); obj.app.labelA(9) = GUI.APP(15,[1 2],uilabel(obj.app.grid(1),'Text','Number of results','FontColor',[.15 .6 .2],'FontWeight','bold','Tooltip','Number of populations saved in each run')); obj.app.editA(2) = GUI.APP(15,3,uieditfield(obj.app.grid(1),'numeric','Value',1,'limits',[1 inf],'RoundFractionalValues','on','Tooltip','Number of populations saved in each run')); tempGrid = GUI.APP(16,[1 3],uigridlayout(obj.app.grid(1),'RowHeight',{'1x'},'ColumnWidth',{'0.5x',20,'1x'},'Padding',[0 0 0 0],'RowSpacing',0,'ColumnSpacing',5,'BackgroundColor','w')); obj.app.labelA(10) = GUI.APP(1,1,uilabel(tempGrid,'Text','File path','FontColor',[.15 .6 .2],'FontWeight','bold','Tooltip','File path for saving experimental settings')); - obj.app.buttonA = GUI.APP(1,2,uibutton(tempGrid,'Text','...','BackgroundColor','w','ButtonpushedFcn',@obj.cb_filepath)); + obj.app.buttonA = GUI.APP(1,2,uibutton(tempGrid,'Text','...','BackgroundColor','w','ButtonpushedFcn',@obj.cb_filepath,'Tooltip','File path for saving experimental settings')); obj.app.editA(3) = GUI.APP(1,3,uieditfield(tempGrid,'Value',fullfile(cd,'Data','Setting.mat'),'HorizontalAlignment','right','Tooltip','File path for saving experimental settings')); % The second panel obj.app.listB = uilist(obj.app.maingrid,obj.GUI.app.figure,obj.GUI.icon); obj.app.grid(2) = GUI.APP(2,3,obj.app.listB.grid); + obj.app.listA(1).ValueChangedFcn = @(~,~)GUI.UpdateAlgProPara(obj.GUI.app.figure,obj.app.listA(1),obj.app.listB,'ALGORITHM',2); + obj.app.listA(2).ValueChangedFcn = @(~,~)GUI.UpdateAlgProPara(obj.GUI.app.figure,obj.app.listA(2),obj.app.listB,'PROBLEM',-2); % The third panel obj.app.grid(3) = GUI.APP(2,5,uigridlayout(obj.app.maingrid,'RowHeight',{30,'1x',30},'ColumnWidth',{230,'1x','1x',230},'Padding',[20 10 20 0],'ColumnSpacing',20,'BackgroundColor','w')); @@ -84,7 +67,7 @@ obj.app.dropC(3) = GUI.APP([1 3],9,uidropdown(tempGrid,'BackgroundColor',[.95 .95 1],'Tooltip','Perform the Wilcoxon rank sum test','Items',{'none','Signed rank test','Rank sum test','Friedman test'},'ItemsData',1:4,'Value',3,'Interruptible','off','BusyAction','cancel','ValueChangedFcn',@obj.TableUpdate)); obj.app.dropC(4) = GUI.APP([1 3],10,uidropdown(tempGrid,'BackgroundColor',[.95 .95 1],'Tooltip','Highlight the best result','Items',{'none','Highlight the best','Highlight all the bests'},'ItemsData',1:3,'Value',2,'Interruptible','off','BusyAction','cancel','ValueChangedFcn',@obj.TableUpdate)); obj.app.table = GUI.APP(2,[1 4],uitable(obj.app.grid(3),'CellSelectionCallback',@obj.cb_tableSelect)); - obj.app.checkC = GUI.APP(3,1,uicheckbox(obj.app.grid(3),'Text','Parallel execution','Tooltip','Perform the experiment with multiple CPUs','Value',0,'Enable',~isempty(ver('parallel')))); + obj.app.checkC = GUI.APP(3,1,uicheckbox(obj.app.grid(3),'Text','Parallel execution','Tooltip','Perform the experiment with multiple CPUs','Enable',~isempty(ver('parallel')))); obj.app.buttonC(1) = GUI.APP(3,2,uibutton(obj.app.grid(3),'push','Text','Start','FontSize',16,'ButtonpushedFcn',@obj.cb_start)); obj.app.buttonC(2) = GUI.APP(3,3,uibutton(obj.app.grid(3),'push','Text','Stop','FontSize',16,'Enable','off','ButtonpushedFcn',@obj.cb_stop)); obj.app.labelC = GUI.APP(3,4,uilabel(obj.app.grid(3),'Text','','HorizontalAlignment','right','VerticalAlignment','center')); @@ -101,67 +84,19 @@ methods(Access = private) %% Update the algorithms and problems in the lists function cb_filter(obj,~,~,index) - if index < 4 - [obj.app.stateA(1:3).Value] = deal(0); - obj.app.stateA(index).Value = 1; - elseif index < 9 - [obj.app.stateA(4:8).Value] = deal(0); - obj.app.stateA(index).Value = 1; - end - filter = [obj.app.stateA.Value]; - func = @(s)all(any(repmat([true,filter],size(s,1),1)&s,2)) && all((any(s(:,2:end),1)&filter)==filter); - % Update the list of algorithms - show = cellfun(func,obj.GUI.algList(:,1)); - obj.app.listA(1).Items = ['(Open File)';obj.GUI.algList(show,2)]; - obj.app.listA(1).Value = {}; - obj.app.labelA(5).Text = sprintf('%d / %d',sum(show),length(show)); - % Update the list of problems - show = cellfun(func,obj.GUI.proList(:,1)); - obj.app.listA(2).Items = ['(Open File)';obj.GUI.proList(show,2)]; - obj.app.listA(2).Value = {}; - obj.app.labelA(7).Text = sprintf('%d / %d',sum(show),length(show)); - % Update the lists of metrics - show = cellfun(@(s)func(s(2:end,1:end-2)),obj.GUI.metList(:,1)); + % Update the lists of algorithms and problems + func = GUI.UpdateAlgProList(index,obj.app.stateA,obj.app.listA(1),obj.app.labelA(5),obj.GUI.algList,obj.app.listA(2),obj.app.labelA(7),obj.GUI.proList); + % Update the list of metrics + show = cellfun(@(s)func(s(2:end,1:end-2)),obj.GUI.metList(:,1)); obj.app.dropC(1).Items = ['Number of runs';'runtime';obj.GUI.metList(show,2)]; obj.TableUpdate(); end - %% Update the parameter list - function cb_updateList(obj,~,~,type) - if type > 0 - filename = obj.app.listA(1).Value; - tip = 'ALGORITHM'; - else - filename = obj.app.listA(2).Value; - tip = 'PROBLEM'; - end - if contains(filename,'Open File') - [file,path] = uigetfile({'*.m','MATLAB class'},''); - if file ~= 0 - try - filename = fullfile(path,file); - f = fopen(filename); - str = fgetl(f); - fclose(f); - assert(contains(str,['< ',tip])); - addpath(path); - catch - uialert(obj.GUI.app.figure,sprintf('The selected file is not a subclass of %s.',tip),'Error'); - return; - end - else - return; - end - else - filename = [filename,'.m']; - end - obj.app.listB.add(filename,type); - obj.app.listB.flush(); - end %% Load or save experimental settings function success = cb_filepath(obj,~,~,filename) success = false; if nargin < 4 % Load experimental settings [file,folder] = uigetfile({'*.mat','MAT file'},'',fileparts(obj.app.editA(3).Value)); + figure(obj.GUI.app.figure); if ischar(file) try filename = fullfile(folder,file); @@ -220,28 +155,21 @@ function cb_start(obj,~,~) allLabel = any(cell2mat(allList(ismember(allList(:,2),get([obj.app.listB.items.title],'Text')),1)),1); SorM = [allLabel(2),allLabel(3)||allLabel(4)]; assert(SorM(1)~=SorM(2),'Cannot perform single- and multi-objective optimization simultaneously.'); - % Generate the ALGORITHM and PROBLEM object + % Generate the ALGORITHM and PROBLEM objects for i = 1 : length(type) - item = obj.app.listB.items(i); - para = cell(1,length(item.edit)); - for j = 1 : length(para) - if ~isempty(item.edit(j).Value) - para{j} = str2num(item.edit(j).Value); - assert(~isempty(para{j}),'The parameter "%s" of %s is illegal.',item.label(j).Text,item.title.Text); - end - end + [name,para] = GUI.GetParameterSetting(obj.app.listB.items(i)); if type(i) > 0 if ~isParallel - ALG = [ALG,feval(item.title.Text,'parameter',para,'save',obj.app.editA(2).Value,'outputFcn',@obj.outputFcn)]; + ALG = [ALG,feval(name,'parameter',para,'save',obj.app.editA(2).Value,'outputFcn',@obj.outputFcn)]; else - ALG = [ALG,feval(item.title.Text,'parameter',para,'save',obj.app.editA(2).Value,'outputFcn',@(~,~)[])]; + ALG = [ALG,feval(name,'parameter',para,'save',obj.app.editA(2).Value,'outputFcn',@(~,~)[])]; end else len = cellfun(@length,para(1:4)); for j = 1 : max(1,max(len)) paraSub = para; paraSub(len>1) = cellfun(@(s)s(min(end,j)),para(len>1),'UniformOutput',false); - PRO = [PRO,feval(item.title.Text,'N',paraSub{1},'M',paraSub{2},'D',paraSub{3},item.label(4).Text,paraSub{4},'parameter',paraSub(5:end))]; + PRO = [PRO,feval(name,'N',paraSub{1},'M',paraSub{2},'D',paraSub{3},obj.app.listB.items(i).label(4).Text,paraSub{4},'parameter',paraSub(5:end))]; end end end @@ -265,15 +193,10 @@ function cb_start(obj,~,~) obj.app.table.Data = cell(length(PRO),length(ALG)); obj.TableUpdateColumn(); % Update the GUI - [obj.GUI.app.button.Enable] = deal('off'); - [obj.app.stateA.Enable] = deal('off'); - [obj.app.listA.Enable] = deal('off'); - [obj.app.editA(1:2).Enable] = deal('off'); - [obj.app.editA(3).Enable] = deal('off'); - obj.app.buttonA.Enable = deal('off'); - obj.app.listB.Enable = deal('off'); - obj.app.buttonC(1).Text = 'Pause'; - obj.app.buttonC(2).Enable = 'on'; + set([obj.GUI.app.button,obj.app.stateA,obj.app.listA,obj.app.editA,obj.app.buttonA],'Enable',false); + obj.app.listB.Enable = false; + obj.app.buttonC(1).Text = 'Pause'; + obj.app.buttonC(2).Enable = true; drawnow('limitrate'); % Perform the experiment for p = 1 : length(PRO) @@ -282,8 +205,7 @@ function cb_start(obj,~,~) arrayfun(@(r)obj.ResultLoad(p,a,r),1:size(obj.data.result,3)); obj.TableUpdate([],[],p); runIndex = find(reshape(cellfun(@isempty,obj.data.result(p,a,:)),1,[])); - % Run algorithms in sequence - if ~isempty(runIndex) && ~isParallel + if ~isempty(runIndex) && ~isParallel % Run algorithms in sequence try for r = runIndex ALG(a).Solve(PRO(p)); @@ -299,8 +221,7 @@ function cb_start(obj,~,~) obj.cb_stop(); rethrow(err); end - % Run algorithms in parallel - elseif ~isempty(runIndex) + elseif ~isempty(runIndex) % Run algorithms in parallel try Future = arrayfun(@(s)parfeval(@parallelFcn,2,ALG(a),PRO(p)),runIndex); while ~all([Future.Read]) @@ -340,15 +261,10 @@ function cb_start(obj,~,~) end %% Stop the execution function cb_stop(obj,~,~) - [obj.GUI.app.button.Enable] = deal('on'); - [obj.app.stateA.Enable] = deal('on'); - [obj.app.listA.Enable] = deal('on'); - [obj.app.editA(1:2).Enable] = deal('on'); - [obj.app.editA(3).Enable] = deal('on'); - obj.app.buttonA.Enable = deal('on'); - obj.app.listB.Enable = deal('on'); - obj.app.buttonC(1).Text = 'Start'; - obj.app.buttonC(2).Enable = 'off'; + set([obj.GUI.app.button,obj.app.stateA,obj.app.listA,obj.app.editA,obj.app.buttonA],'Enable',true); + obj.app.listB.Enable = true; + obj.app.buttonC(1).Text = 'Start'; + obj.app.buttonC(2).Enable = false; end %% Output function function outputFcn(obj,Algorithm,Problem) @@ -556,6 +472,7 @@ function cb_save(obj,~,~) if ~isempty(obj.app.table.Data) try [Name,Path] = uiputfile({'*.xlsx','Excel table';'*.tex','TeX table';'*.txt','Text file';'*.mat','MAT file'},'','new'); + figure(obj.GUI.app.figure); if ischar(Name) [~,~,Type] = fileparts(Name); switch Type @@ -596,8 +513,7 @@ function cb_tableDisplay(obj,~,~) end end %% Show the results in new figure - function cb_tableShow(obj,ui,~,type) - ui.Parent.Visible = false; + function cb_tableShow(obj,~,~,type) metric = obj.app.dropC(1).Value; loc = obj.app.table.UserData; nRow = loc(3) - loc(1) + 1; diff --git a/PlatEMO/GUI/module_test.m b/PlatEMO/GUI/module_test.m index d07cd29e9..91b3e276c 100644 --- a/PlatEMO/GUI/module_test.m +++ b/PlatEMO/GUI/module_test.m @@ -30,37 +30,20 @@ GUI.APP([1 2],8,uipanel(obj.app.maingrid,'BackgroundColor',[.8 .8 .8])); % The first panel - obj.app.grid(1) = GUI.APP(2,1,uigridlayout(obj.app.maingrid,'RowHeight',{16,19,16,19,19,16,19,19,19,22,'1x',22,'1x'},'ColumnWidth',{'1x','1x','1x'},'Padding',[8 10 8 0],'RowSpacing',3,'ColumnSpacing',5,'BackgroundColor','w')); - obj.app.labelA(1) = GUI.APP(1,[1 3],uilabel(obj.app.grid(1),'Text','Number of objectives','VerticalAlignment','bottom','FontSize',12,'FontColor',[.15 .6 .2],'FontWeight','bold')); - obj.app.stateA(1) = GUI.APP(2,1,uibutton(obj.app.grid(1),'state','Text','single','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The problem has a single objective','ValueChangedFcn',{@obj.cb_filter,1})); - obj.app.stateA(2) = GUI.APP(2,2,uibutton(obj.app.grid(1),'state','Text','multi','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',1,'Tooltip','The problem has 2 or 3 objectives','ValueChangedFcn',{@obj.cb_filter,2})); - obj.app.stateA(3) = GUI.APP(2,3,uibutton(obj.app.grid(1),'state','Text','many','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The problem has 4 or more objectives','ValueChangedFcn',{@obj.cb_filter,3})); - obj.app.labelA(2) = GUI.APP(3,[1 3],uilabel(obj.app.grid(1),'Text','Encoding scheme','VerticalAlignment','bottom','FontSize',12,'FontColor',[.15 .6 .2],'FontWeight','bold')); - obj.app.stateA(4) = GUI.APP(4,1,uibutton(obj.app.grid(1),'state','Text','real','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',1,'Tooltip','The decision variables are real numbers','ValueChangedFcn',{@obj.cb_filter,4})); - obj.app.stateA(5) = GUI.APP(4,2,uibutton(obj.app.grid(1),'state','Text','integer','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The decision variables are integers','ValueChangedFcn',{@obj.cb_filter,5})); - obj.app.stateA(6) = GUI.APP(4,3,uibutton(obj.app.grid(1),'state','Text','label','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The decision variables are labels','ValueChangedFcn',{@obj.cb_filter,6})); - obj.app.stateA(7) = GUI.APP(5,1,uibutton(obj.app.grid(1),'state','Text','binary','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The decision variables are binary numbers','ValueChangedFcn',{@obj.cb_filter,7})); - obj.app.stateA(8) = GUI.APP(5,2,uibutton(obj.app.grid(1),'state','Text','permutation','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The decision vector is a permutation','ValueChangedFcn',{@obj.cb_filter,8})); - obj.app.labelA(3) = GUI.APP(6,[1 3],uilabel(obj.app.grid(1),'Text','Special difficulties','VerticalAlignment','bottom','FontSize',12,'FontColor',[.15 .6 .2],'FontWeight','bold')); - obj.app.stateA(9) = GUI.APP(7,1,uibutton(obj.app.grid(1),'state','Text','large','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The problem has more than 100 decision variables','ValueChangedFcn',{@obj.cb_filter,9})); - obj.app.stateA(10) = GUI.APP(7,2,uibutton(obj.app.grid(1),'state','Text','constrained','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The problem has constraints','ValueChangedFcn',{@obj.cb_filter,10})); - obj.app.stateA(11) = GUI.APP(7,3,uibutton(obj.app.grid(1),'state','Text','expensive','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The objectives are computationally time-consuming','ValueChangedFcn',{@obj.cb_filter,11})); - obj.app.stateA(12) = GUI.APP(8,1,uibutton(obj.app.grid(1),'state','Text','multimodal','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The objectives are multimodal','ValueChangedFcn',{@obj.cb_filter,12})); - obj.app.stateA(13) = GUI.APP(8,2,uibutton(obj.app.grid(1),'state','Text','sparse','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','Most decision variables of the optimal solutions are zero','ValueChangedFcn',{@obj.cb_filter,13})); - obj.app.stateA(14) = GUI.APP(8,3,uibutton(obj.app.grid(1),'state','Text','dynamic','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The objectives vary periodically','ValueChangedFcn',{@obj.cb_filter,14})); - obj.app.stateA(15) = GUI.APP(9,1,uibutton(obj.app.grid(1),'state','Text','multitask','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The problem has multiple tasks to be solved simultaneously','ValueChangedFcn',{@obj.cb_filter,15})); - obj.app.stateA(16) = GUI.APP(9,2,uibutton(obj.app.grid(1),'state','Text','bilevel','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The problem has two nested objectives','ValueChangedFcn',{@obj.cb_filter,16})); - obj.app.stateA(17) = GUI.APP(9,3,uibutton(obj.app.grid(1),'state','Text','robust','FontSize',11,'FontColor',[.15 .6 .2],'BackgroundColor','w','Value',0,'Tooltip','The objectives are influenced by uncertain factors','ValueChangedFcn',{@obj.cb_filter,17})); - obj.app.labelA(4) = GUI.APP(10,[1 2],uilabel(obj.app.grid(1),'Text','Algorithms','VerticalAlignment','bottom','FontSize',13,'FontColor',[.2 .4 .7],'FontWeight','bold')); - obj.app.labelA(5) = GUI.APP(10,3,uilabel(obj.app.grid(1),'HorizontalAlignment','right','VerticalAlignment','bottom','FontSize',10,'FontColor',[.2 .4 .7])); - obj.app.listA(1) = GUI.APP(11,[1 3],uilistbox(obj.app.grid(1),'FontColor',[.2 .4 .7],'ValueChangedFcn',{@obj.cb_updateList,1})); - obj.app.labelA(6) = GUI.APP(12,[1 2],uilabel(obj.app.grid(1),'Text','Problems','VerticalAlignment','bottom','FontSize',13,'FontColor',[.9 .5 .2],'FontWeight','bold')); - obj.app.labelA(7) = GUI.APP(12,3,uilabel(obj.app.grid(1),'HorizontalAlignment','right','VerticalAlignment','bottom','FontSize',10,'FontColor',[.9 .5 .2])); - obj.app.listA(2) = GUI.APP(13,[1 3],uilistbox(obj.app.grid(1),'FontColor',[.9 .5 .2],'ValueChangedFcn',{@obj.cb_updateList,-1})); + obj.app.grid(1) = GUI.APP(2,1,uigridlayout(obj.app.maingrid,'RowHeight',{16,19,16,19,19,16,19,19,19,22,'1x',22,'1x'},'ColumnWidth',{'1x','1x','1x'},'Padding',[8 10 8 0],'RowSpacing',3,'ColumnSpacing',5,'BackgroundColor','w')); + [obj.app.stateA,obj.app.labelA] = GUI.GenerateLabelButton(obj.app.grid(1),[0,1,0,1,zeros(1,13)],@obj.cb_filter); + obj.app.labelA(4) = GUI.APP(10,[1 2],uilabel(obj.app.grid(1),'Text','Algorithms','VerticalAlignment','bottom','FontSize',13,'FontColor',[.2 .4 .7],'FontWeight','bold')); + obj.app.labelA(5) = GUI.APP(10,3,uilabel(obj.app.grid(1),'HorizontalAlignment','right','VerticalAlignment','bottom','FontSize',10,'FontColor',[.2 .4 .7])); + obj.app.listA(1) = GUI.APP(11,[1 3],uilistbox(obj.app.grid(1),'FontColor',[.2 .4 .7])); + obj.app.labelA(6) = GUI.APP(12,[1 2],uilabel(obj.app.grid(1),'Text','Problems','VerticalAlignment','bottom','FontSize',13,'FontColor',[.9 .5 .2],'FontWeight','bold')); + obj.app.labelA(7) = GUI.APP(12,3,uilabel(obj.app.grid(1),'HorizontalAlignment','right','VerticalAlignment','bottom','FontSize',10,'FontColor',[.9 .5 .2])); + obj.app.listA(2) = GUI.APP(13,[1 3],uilistbox(obj.app.grid(1),'FontColor',[.9 .5 .2])); % The second panel obj.app.listB = uilist(obj.app.maingrid,obj.GUI.app.figure,obj.GUI.icon); obj.app.grid(2) = GUI.APP(2,3,obj.app.listB.grid); + obj.app.listA(1).ValueChangedFcn = @(~,~)GUI.UpdateAlgProPara(obj.GUI.app.figure,obj.app.listA(1),obj.app.listB,'ALGORITHM',1); + obj.app.listA(2).ValueChangedFcn = @(~,~)GUI.UpdateAlgProPara(obj.GUI.app.figure,obj.app.listA(2),obj.app.listB,'PROBLEM',-1); % The third panel obj.app.dropC(1) = GUI.APP(1,6,uidropdown(obj.app.maingrid,'BackgroundColor','w','Items',{},'ValueChangedFcn',@obj.cb_slider)); @@ -74,7 +57,7 @@ obj.app.slider = GUI.APP(2,[1 7],uislider(obj.app.grid(3),'Limits',[0 1],'MajorTicks',0:0.25:1,'MajorTickLabels',{'0%','25%','50%','75%','100%'},'MinorTicks',0:0.01:1,'ValueChangedFcn',@obj.cb_slider)); obj.app.labelC = GUI.APP(3,[1 2],uilabel(obj.app.grid(3),'Text','','HorizontalAlignment','left')); obj.app.buttonC(1) = GUI.APP(3,3,uibutton(obj.app.grid(3),'push','Text','Start','FontSize',16,'ButtonpushedFcn',@obj.cb_start)); - obj.app.buttonC(2) = GUI.APP(3,4,uibutton(obj.app.grid(3),'push','Text','Stop','FontSize',16,'Enable','off','ButtonpushedFcn',@obj.cb_stop)); + obj.app.buttonC(2) = GUI.APP(3,4,uibutton(obj.app.grid(3),'push','Text','Stop','FontSize',16,'Enable','off','ButtonpushedFcn',@(~,~)set(obj.app.buttonC(1:2),{'Enable','Text'},{true,'Start';false,'Stop'}))); obj.app.menuC = uicontext(obj.GUI.app.figure,120); obj.app.menuC.add(' Save best solutions','',{@obj.cb_save,1}); obj.app.menuC.add(' Save all solutions','',{@obj.cb_save,2}); @@ -93,76 +76,25 @@ obj.cb_filter([],[],2); obj.app.listA(1).Value = 'NSGAII'; obj.app.listA(2).Value = 'DTLZ2'; - obj.cb_updateList([],[],1); - obj.cb_updateList([],[],-1); + GUI.UpdateAlgProPara(obj.GUI.app.figure,obj.app.listA(1),obj.app.listB,'ALGORITHM',1); + GUI.UpdateAlgProPara(obj.GUI.app.figure,obj.app.listA(2),obj.app.listB,'PROBLEM',-1); end end methods(Access = private) %% Update the algorithms and problems in the lists function cb_filter(obj,~,~,index) - if index < 4 - [obj.app.stateA(1:3).Value] = deal(0); - obj.app.stateA(index).Value = 1; - elseif index < 9 - [obj.app.stateA(4:8).Value] = deal(0); - obj.app.stateA(index).Value = 1; - end - filter = [obj.app.stateA.Value]; - func = @(s)all(any(repmat([true,filter],size(s,1),1)&s,2)) && all((any(s(:,2:end),1)&filter)==filter); - % Update the list of algorithms - show = cellfun(func,obj.GUI.algList(:,1)); - obj.app.listA(1).Items = ['(Open File)';obj.GUI.algList(show,2)]; - obj.app.listA(1).Value = {}; - obj.app.labelA(5).Text = sprintf('%d / %d',sum(show),length(show)); - % Update the list of problems - show = cellfun(func,obj.GUI.proList(:,1)); - obj.app.listA(2).Items = ['(Open File)';obj.GUI.proList(show,2)]; - obj.app.listA(2).Value = {}; - obj.app.labelA(7).Text = sprintf('%d / %d',sum(show),length(show)); - % Update the lists of metrics - show = cellfun(@(s)func(s(2:end,1:end-2)),obj.GUI.metList(:,1)); + % Update the lists of algorithms and problems + func = GUI.UpdateAlgProList(index,obj.app.stateA,obj.app.listA(1),obj.app.labelA(5),obj.GUI.algList,obj.app.listA(2),obj.app.labelA(7),obj.GUI.proList); + % Update the list of metrics + show = cellfun(@(s)func(s(2:end,1:end-2)),obj.GUI.metList(:,1)); if obj.app.stateA(1).Value == 0 % Multi-objective optimization obj.app.dropC(1).Items = ['Population (objectives)';'Population (variables)';'True Pareto front';obj.GUI.metList(show,2)]; obj.app.dropD(2).Items = ['runtime';obj.GUI.metList(show,2)]; - obj.cb_dropdown2(); else % Single-objective optimization obj.app.dropC(2).Items = ['Population (variables)';obj.GUI.metList(show,2)]; obj.app.dropD(3).Items = ['runtime';obj.GUI.metList(show,2)]; - obj.cb_dropdown2(); - end - end - %% Update the parameter list - function cb_updateList(obj,~,~,type) - if type > 0 - filename = obj.app.listA(1).Value; - tip = 'ALGORITHM'; - else - filename = obj.app.listA(2).Value; - tip = 'PROBLEM'; - end - if contains(filename,'Open File') - [file,path] = uigetfile({'*.m','MATLAB class'},''); - if file ~= 0 - try - filename = fullfile(path,file); - f = fopen(filename); - str = fgetl(f); - fclose(f); - assert(contains(str,['< ',tip])); - addpath(path); - catch - uialert(obj.GUI.app.figure,sprintf('The selected file is not a subclass of %s.',tip),'Error'); - return; - end - else - return; - end - else - filename = [filename,'.m']; end - obj.app.listB.del([],type); - obj.app.listB.add(filename,type); - obj.app.listB.flush(); + obj.cb_dropdown2(); end %% Start the execution function cb_start(obj,~,~) @@ -171,23 +103,12 @@ function cb_start(obj,~,~) elseif strcmp(obj.app.buttonC(1).Text,'Continue') obj.app.buttonC(1).Text = 'Pause'; else + % Generate the ALGORITHM and PROBLEM objects try - % Generate the ALGORITHM and PROBLEM object - for i = 1 : 2 - item = obj.app.listB.items(i); - para = cell(1,length(item.edit)); - for j = 1 : length(para) - if ~isempty(item.edit(j).Value) - para{j} = str2num(item.edit(j).Value); - assert(~isempty(para{j}),'The parameter "%s" of %s is illegal.',item.label(j).Text,item.title.Text); - end - end - if i == 1 - ALG = feval(item.title.Text,'parameter',para,'outputFcn',@obj.outputFcn,'save',inf); - else - PRO = feval(item.title.Text,'N',para{1},'M',para{2},'D',para{3},item.label(4).Text,para{4},'parameter',para(5:end)); - end - end + [name,para] = GUI.GetParameterSetting(obj.app.listB.items(1)); + ALG = feval(name,'parameter',para,'outputFcn',@obj.outputFcn,'save',20); + [name,para] = GUI.GetParameterSetting(obj.app.listB.items(2)); + PRO = feval(name,'N',para{1},'M',para{2},'D',para{3},obj.app.listB.items(2).label(4).Text,para{4},'parameter',para(5:end)); catch err uialert(obj.GUI.app.figure,err.message,'Invalid parameter settings'); return; @@ -203,34 +124,24 @@ function cb_start(obj,~,~) end obj.data = [obj.data;{ALG},{PRO},{str}]; % Update the GUI - [obj.GUI.app.button.Enable] = deal('off'); - [obj.app.stateA.Enable] = deal('off'); - [obj.app.listA.Enable] = deal('off'); - [obj.app.toolC(1:2).Visible] = deal('off'); - [obj.app.dropD.Enable] = deal('off'); - obj.app.listB.Enable = 'off'; - obj.app.buttonC(1).Text = 'Pause'; - obj.app.buttonC(2).Enable = 'on'; - obj.app.buttonC(3).Enable = 'on'; + set([obj.GUI.app.button,obj.app.stateA,obj.app.listA,obj.app.dropD,obj.app.dropC],'Enable',false); + obj.app.listB.Enable = false; + set(obj.app.toolC,'Visible',false); + obj.app.buttonC(1).Text = 'Pause'; + set(obj.app.buttonC([2,3]),'Enable',true); if PRO.M > 1 - obj.app.dropC(1).Value = obj.app.dropC(1).Items{1}; - obj.app.dropC(1).Visible = 'on'; - obj.app.dropC(2).Visible = 'off'; - obj.app.dropD(2).Visible = 'on'; - obj.app.dropD(3).Visible = 'off'; + obj.app.dropC(1).Value = obj.app.dropC(1).Items{1}; + set([obj.app.dropC(1),obj.app.dropD(2)],'Visible',true); + set([obj.app.dropC(2),obj.app.dropD(3)],'Visible',false); else - obj.app.dropC(2).Value = obj.app.dropC(2).Items{1}; - obj.app.dropC(1).Visible = 'off'; - obj.app.dropC(2).Visible = 'on'; - obj.app.dropD(2).Visible = 'off'; - obj.app.dropD(3).Visible = 'on'; + obj.app.dropC(2).Value = obj.app.dropC(2).Items{1}; + set([obj.app.dropC(1),obj.app.dropD(2)],'Visible',false); + set([obj.app.dropC(2),obj.app.dropD(3)],'Visible',true); end - obj.app.dropC(1).Enable = 'off'; - obj.app.dropC(2).Enable = 'off'; - obj.app.dropD(1).Items = [obj.app.dropD(1).Items,sprintf('%s on %s',class(ALG),class(PRO))]; - obj.app.dropD(1).Value = length(obj.app.dropD(1).Items); - obj.app.labelD.Text = ''; - obj.app.textD.Value = ''; + obj.app.dropD(1).Items = [obj.app.dropD(1).Items,sprintf('%s on %s',class(ALG),class(PRO))]; + obj.app.dropD(1).Value = length(obj.app.dropD(1).Items); + obj.app.labelD.Text = ''; + obj.app.textD.Value = ''; % Execute the algorithm try ALG.Solve(PRO); @@ -244,16 +155,11 @@ function cb_start(obj,~,~) end %% Stop the execution function cb_stop(obj,~,~) - [obj.GUI.app.button.Enable] = deal('on'); - [obj.app.stateA.Enable] = deal('on'); - [obj.app.listA.Enable] = deal('on'); - [obj.app.toolC(1:2).Visible] = deal('on'); - [obj.app.dropD.Enable] = deal('on'); - obj.app.listB.Enable = 'on'; - obj.app.buttonC(1).Text = 'Start'; - obj.app.buttonC(2).Enable = 'off'; - obj.app.dropC(1).Enable = 'on'; - obj.app.dropC(2).Enable = 'on'; + set([obj.GUI.app.button,obj.app.stateA,obj.app.listA,obj.app.dropD,obj.app.dropC],'Enable',true); + obj.app.listB.Enable = true; + set(obj.app.toolC,'Visible',true); + obj.app.buttonC(1).Text = 'Start'; + obj.app.buttonC(2).Enable = false; if isempty(obj.data{end,1}.result) obj.data(end,:) = []; obj.app.dropD(1).Items(end) = []; @@ -266,30 +172,7 @@ function cb_save(obj,~,~,type) PRO = obj.data{obj.app.dropD(1).Value,2}; rate = PRO.FE/max(PRO.FE,PRO.maxFE); index = max(1,round(obj.app.slider.Value/rate*size(ALG.result,1))); - Pop = ALG.result{index,2}; - if type == 1 - Pop = Pop.best; - end - if isempty(Pop) - uialert(obj.GUI.app.figure,'No solution can be saved.','Error'); - else - Data = [Pop.decs,Pop.objs,Pop.cons]; - try - [Name,Path] = uiputfile({'*.txt','Text file';'*.dat','Text file';'*.csv','Text file';'*.mat','MAT file';'*.xlsx','Excel table'},'','data'); - if ischar(Name) - [~,~,Type] = fileparts(Name); - switch Type - case '.mat' - save(fullfile(Path,Name),'Data','-mat'); - otherwise - writematrix(Data,fullfile(Path,Name)); - end - end - catch err - uialert(obj.GUI.app.figure,'Fail to save the result, please refer to the command window for details.','Error'); - rethrow(err); - end - end + GUI.SavePopulation(obj.GUI.app.figure,ALG.result{index,2},type); end %% Output function function outputFcn(obj,Algorithm,Problem) @@ -365,6 +248,7 @@ function cb_slider(obj,~,~,ax) function cb_toolbutton1(obj,~,~) if ~isempty(obj.app.dropD(1).Items) [file,folder] = uiputfile({'*.gif','GIF image'},''); + figure(obj.GUI.app.figure); if file ~= 0 try filename = fullfile(folder,file); @@ -413,15 +297,11 @@ function cb_dropdown1(obj,~,~) obj.cb_slider(); obj.app.textD.Value = obj.data{obj.app.dropD(1).Value,3}; if obj.data{obj.app.dropD(1).Value,2}.M > 1 - obj.app.dropC(1).Visible = 'on'; - obj.app.dropC(2).Visible = 'off'; - obj.app.dropD(2).Visible = 'on'; - obj.app.dropD(3).Visible = 'off'; + set([obj.app.dropC(1),obj.app.dropD(2)],'Visible',true); + set([obj.app.dropC(2),obj.app.dropD(3)],'Visible',false); else - obj.app.dropC(1).Visible = 'off'; - obj.app.dropC(2).Visible = 'on'; - obj.app.dropD(2).Visible = 'off'; - obj.app.dropD(3).Visible = 'on'; + set([obj.app.dropC(1),obj.app.dropD(2)],'Visible',false); + set([obj.app.dropC(2),obj.app.dropD(3)],'Visible',true); end end end diff --git a/PlatEMO/GUI/uicontext.m b/PlatEMO/GUI/uicontext.m index 64a46c8d9..8d1c32246 100644 --- a/PlatEMO/GUI/uicontext.m +++ b/PlatEMO/GUI/uicontext.m @@ -25,11 +25,11 @@ end %% Add a new item function add(obj,str,icon,cb) - obj.items = [obj.items,uibutton(obj.panel,'Position',[-2 0 obj.panel.Position(3)+5 25],'Text',str,'HorizontalAlignment','left','BackgroundColor',[.94 .94 .94],'Icon',icon,'ButtonPushedFcn',cb)]; + obj.items = [obj.items,uibutton(obj.panel,'Position',[-2 0 obj.panel.Position(3)+5 25],'Text',str,'HorizontalAlignment','left','BackgroundColor',[.94 .94 .94],'Icon',icon,'ButtonPushedFcn',@obj.cb_callback,'UserData',cb)]; obj.gaps = [obj.gaps,uipanel(obj.panel,'Position',[-2 0 obj.panel.Position(3)+5 3],'BorderType','none')]; obj.panel.Children = obj.panel.Children([1,3:end,2]); end - %% Flush the menu + %% Refresh the menu function flush(obj) if ~isempty(obj.items) obj.panel.Position(4) = 23*length(obj.items) - 2; @@ -65,6 +65,18 @@ function cb_motion(obj,~,~) if obj.fig.CurrentPoint(1)sum(obj.panel.Position([1,3])) || obj.fig.CurrentPoint(2)sum(obj.panel.Position([2,4])) obj.panel.Visible = false; delete(obj.listener); + obj.listener = []; + end + end + %% Callback of menus + function cb_callback(obj,h,event) + obj.panel.Visible = false; + delete(obj.listener); + obj.listener = []; + if iscell(h.UserData) + h.UserData{1}(h,event,h.UserData{2:end}); + else + h.UserData(h,event); end end end diff --git a/PlatEMO/GUI/uilist.m b/PlatEMO/GUI/uilist.m index aeeb54866..281d1f038 100644 --- a/PlatEMO/GUI/uilist.m +++ b/PlatEMO/GUI/uilist.m @@ -130,7 +130,10 @@ function add(obj,filename,type) %% Delete existing items function del(obj,index,type) if ~isempty(obj.items) - if nargin >= 3 + if nargin > 2 + if type == 3 + type = 1; + end index = find([obj.items.type]==type); end for i = index @@ -143,7 +146,7 @@ function del(obj,index,type) obj.items(index) = []; end end - %% Flush the list + %% Refresh the list function flush(obj) if ~isempty(obj.items) % Sort the items @@ -225,39 +228,33 @@ function cb_switch(obj,ui,~) end end %% Open file - function cb_openfile(obj,ui,~) - ui.Parent.Visible = false; + function cb_openfile(obj,~,~) web(['file://',which(obj.items(obj.current).title.Text)],'-browser'); end %% Open folder - function cb_openfolder(obj,ui,~) - ui.Parent.Visible = false; + function cb_openfolder(obj,~,~) web(['file://',fileparts(which(obj.items(obj.current).title.Text))],'-browser'); end %% Search online - function cb_search(obj,ui,~) - ui.Parent.Visible = false; + function cb_search(obj,~,~) web(['https://scholar.google.com/scholar?q=%',strjoin(cellstr(dec2hex(double(obj.items(obj.current).cite))),'%')],'-browser'); end %% Move the item up - function cb_moveup(obj,ui,~) - ui.Parent.Visible = false; + function cb_moveup(obj,~,~) if obj.current > 1 obj.items = obj.items([1:obj.current-2,obj.current,obj.current-1,obj.current+1:end]); obj.flush(); end end %% Move the item down - function cb_movedown(obj,ui,~) - ui.Parent.Visible = false; + function cb_movedown(obj,~,~) if obj.current < length(obj.items) obj.items = obj.items([1:obj.current-1,obj.current+1,obj.current,obj.current+2:end]); obj.flush(); end end %% Delete the item - function cb_delete(obj,ui,~) - ui.Parent.Visible = false; + function cb_delete(obj,~,~) obj.del(obj.current); obj.flush(); end diff --git a/PlatEMO/Problems/Multi-objective optimization/FDA/FDA2.m b/PlatEMO/Problems/Multi-objective optimization/FDA/FDA2.m index 58175666d..3754cb478 100644 --- a/PlatEMO/Problems/Multi-objective optimization/FDA/FDA2.m +++ b/PlatEMO/Problems/Multi-objective optimization/FDA/FDA2.m @@ -47,10 +47,10 @@ function Setting(obj) function PopObj = CalObj(obj,PopDec) PopObj(:,1) = PopDec(:,1); t = floor(obj.FE/obj.N/obj.taut)/obj.nt; - g = 1 + sum(PopDec(:,2:(end-1)/2).^2,2); + g = 1 + sum(PopDec(:,2:(end+1)/2).^2,2); H = 0.75 + 0.7*sin(0.5.*pi.*t); % Note: The original definition of h is questionable - h = 1 - (PopObj(:,1)./g).^(H+sum((PopDec(:,(end-1)/2+1:end)-H).^2,2)); + h = 1 - (PopObj(:,1)./g).^(H+sum((PopDec(:,(end+1)/2+1:end)-H).^2,2)); PopObj(:,2) = g.*h; end %% Generate points on the Pareto front diff --git a/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP1.m b/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP1.m new file mode 100644 index 000000000..2d46966e1 --- /dev/null +++ b/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP1.m @@ -0,0 +1,87 @@ +classdef GLSMOP1 < PROBLEM +% +% General large-scale benchmark MOP +% nk --- 5 --- Number of subcomponents in each variable group + +%------------------------------- Reference -------------------------------- +% H. Gu, H. Wang, and J. Ma, Test suites and performance of algorithms in +% large-scale multiobjective evolutionary optimization, Proceedings of the +% IEEE Congress on Evolutionary Computation, 2024. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Haoran Gu + + properties(Access = private) + nk = 5; % Number of subcomponents in each variable group + sublen; % Number of variables in each subcomponent + len; % Cumulative sum of lengths of variable groups + end + methods + %% Default settings of the problem + function Setting(obj) + % Parameter setting + obj.nk = obj.ParameterSet(5); + if isempty(obj.M); obj.M = 3; end + if isempty(obj.D); obj.D = 100*obj.M; end + obj.lower = zeros(1,obj.D); + obj.upper = [ones(1,obj.M-1),10.*ones(1,obj.D-obj.M+1)]; + obj.encoding = ones(1,obj.D); + % Calculate the number of variables in each subcomponent + c = 3.8*0.1*(1-0.1); + for i = 1 : obj.M-1 + c = [c,3.8.*c(end).*(1-c(end))]; + end + obj.sublen = floor(c./sum(c).*(obj.D-obj.M+1)/obj.nk); + obj.len = [0,cumsum(obj.sublen*obj.nk)]; + end + %% Calculate objective values + function PopObj = CalObj(obj,PopDec) + [N,D] = size(PopDec); + M = obj.M; + %% Change PS by the transfer function + for i = M : 2 : D + PopDec(:,i) = obj.lower(i) + obj.upper(i)-PopDec(:,i); + end + PopDec(:,M:D) = (1+repmat((M:D)./D,N,1)).*PopDec(:,M:D) - repmat(PopDec(:,1)*10,1,D-M+1); + G = zeros(N,M); + for i = 1 : 2 : M + for j = 1 : obj.nk + G(:,i) = G(:,i) + Sphere(PopDec(:,obj.len(i)+M-1+(j-1)*obj.sublen(i)+1:obj.len(i)+M-1+j*obj.sublen(i))); + end + end + for i = 2 : 2 : M + for j = 1 : obj.nk + G(:,i) = G(:,i) + Sphere(PopDec(:,obj.len(i)+M-1+(j-1)*obj.sublen(i)+1:obj.len(i)+M-1+j*obj.sublen(i))); + end + end + G = G./repmat(obj.sublen,N,1)./obj.nk; + PopObj = (1+G).*fliplr(cumprod([ones(N,1),PopDec(:,1:M-1)],2)).*[ones(N,1),1-PopDec(:,M-1:-1:1)]; + end + %% Generate points on the Pareto front + function R = GetOptimum(obj,N) + R = UniformPoint(N,obj.M); + end + %% Generate the image of Pareto front + function R = GetPF(obj) + if obj.M == 2 + R = obj.GetOptimum(100); + elseif obj.M == 3 + a = linspace(0,1,10)'; + R = {a*a',a*(1-a'),(1-a)*ones(size(a'))}; + else + R = []; + end + end + end +end + +function f = Sphere(x) + f = sum(x.^2,2); +end \ No newline at end of file diff --git a/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP2.m b/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP2.m new file mode 100644 index 000000000..7e277039f --- /dev/null +++ b/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP2.m @@ -0,0 +1,91 @@ +classdef GLSMOP2 < PROBLEM +% +% General large-scale benchmark MOP +% nk --- 5 --- Number of subcomponents in each variable group + +%------------------------------- Reference -------------------------------- +% H. Gu, H. Wang, and J. Ma, Test suites and performance of algorithms in +% large-scale multiobjective evolutionary optimization, Proceedings of the +% IEEE Congress on Evolutionary Computation, 2024. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Haoran Gu + + properties(Access = private) + nk = 5; % Number of subcomponents in each variable group + sublen; % Number of variables in each subcomponent + len; % Cumulative sum of lengths of variable groups + end + methods + %% Default settings of the problem + function Setting(obj) + % Parameter setting + obj.nk = obj.ParameterSet(5); + if isempty(obj.M); obj.M = 3; end + if isempty(obj.D); obj.D = 100*obj.M; end + obj.lower = zeros(1,obj.D); + obj.upper = [ones(1,obj.M-1),10.*ones(1,obj.D-obj.M+1)]; + obj.encoding = ones(1,obj.D); + % Calculate the number of variables in each subcomponent + c = 3.8*0.1*(1-0.1); + for i = 1 : obj.M-1 + c = [c,3.8.*c(end).*(1-c(end))]; + end + obj.sublen = floor(c./sum(c).*(obj.D-obj.M+1)/obj.nk); + obj.len = [0,cumsum(obj.sublen*obj.nk)]; + end + %% Calculate objective values + function PopObj = CalObj(obj,PopDec) + [N,D] = size(PopDec); + M = obj.M; + %% Change PS by the transfer function + for i = M : 2 : D + PopDec(:,i) = obj.lower(i) + obj.upper(i)-PopDec(:,i); + end + PopDec(:,M:D) = (1+repmat((M:D)./D,N,1)).*PopDec(:,M:D) - repmat(PopDec(:,1)*10,1,D-M+1); + G = zeros(N,M); + for i = 1 : 2 : M + for j = 1 : obj.nk + G(:,i) = G(:,i) + Griewank(PopDec(:,obj.len(i)+M-1+(j-1)*obj.sublen(i)+1:obj.len(i)+M-1+j*obj.sublen(i))); + end + end + for i = 2 : 2 : M + for j = 1 : obj.nk + G(:,i) = G(:,i) + Schwefel(PopDec(:,obj.len(i)+M-1+(j-1)*obj.sublen(i)+1:obj.len(i)+M-1+j*obj.sublen(i))); + end + end + G = G./repmat(obj.sublen,N,1)./obj.nk; + PopObj = (1+G).*fliplr(cumprod([ones(N,1),PopDec(:,1:M-1)],2)).*[ones(N,1),1-PopDec(:,M-1:-1:1)]; + end + %% Generate points on the Pareto front + function R = GetOptimum(obj,N) + R = UniformPoint(N,obj.M); + end + %% Generate the image of Pareto front + function R = GetPF(obj) + if obj.M == 2 + R = obj.GetOptimum(100); + elseif obj.M == 3 + a = linspace(0,1,10)'; + R = {a*a',a*(1-a'),(1-a)*ones(size(a'))}; + else + R = []; + end + end + end +end + +function f = Griewank(x) + f = sum(x.^2,2)./4000 - prod(cos(x./repmat(sqrt(1:size(x,2)),size(x,1),1)),2) + 1; +end + +function f = Schwefel(x) + f = max(abs(x),[],2); +end \ No newline at end of file diff --git a/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP3.m b/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP3.m new file mode 100644 index 000000000..443c998b9 --- /dev/null +++ b/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP3.m @@ -0,0 +1,91 @@ +classdef GLSMOP3 < PROBLEM +% +% General large-scale benchmark MOP +% nk --- 5 --- Number of subcomponents in each variable group + +%------------------------------- Reference -------------------------------- +% H. Gu, H. Wang, and J. Ma, Test suites and performance of algorithms in +% large-scale multiobjective evolutionary optimization, Proceedings of the +% IEEE Congress on Evolutionary Computation, 2024. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Haoran Gu + + properties(Access = private) + nk = 5; % Number of subcomponents in each variable group + sublen; % Number of variables in each subcomponent + len; % Cumulative sum of lengths of variable groups + end + methods + %% Default settings of the problem + function Setting(obj) + % Parameter setting + obj.nk = obj.ParameterSet(5); + if isempty(obj.M); obj.M = 3; end + if isempty(obj.D); obj.D = 100*obj.M; end + obj.lower = zeros(1,obj.D); + obj.upper = [ones(1,obj.M-1),10.*ones(1,obj.D-obj.M+1)]; + obj.encoding = ones(1,obj.D); + % Calculate the number of variables in each subcomponent + c = 3.8*0.1*(1-0.1); + for i = 1 : obj.M-1 + c = [c,3.8.*c(end).*(1-c(end))]; + end + obj.sublen = floor(c./sum(c).*(obj.D-obj.M+1)/obj.nk); + obj.len = [0,cumsum(obj.sublen*obj.nk)]; + end + %% Calculate objective values + function PopObj = CalObj(obj,PopDec) + [N,D] = size(PopDec); + M = obj.M; + %% Change PS by the transfer function + for i = M : 2 : D + PopDec(:,i) = obj.lower(i) + obj.upper(i)-PopDec(:,i); + end + PopDec(:,M:D) = (1+repmat((M:D)./D,N,1)).*PopDec(:,M:D) - repmat(PopDec(:,1)*10,1,D-M+1); + G = zeros(N,M); + for i = 1 : 2 : M + for j = 1 : obj.nk + G(:,i) = G(:,i) + Rastrigin(PopDec(:,obj.len(i)+M-1+(j-1)*obj.sublen(i)+1:obj.len(i)+M-1+j*obj.sublen(i))); + end + end + for i = 2 : 2 : M + for j = 1 : obj.nk + G(:,i) = G(:,i) + Rosenbrock(PopDec(:,obj.len(i)+M-1+(j-1)*obj.sublen(i)+1:obj.len(i)+M-1+j*obj.sublen(i))); + end + end + G = G./repmat(obj.sublen,N,1)./obj.nk; + PopObj = (1+G).*fliplr(cumprod([ones(N,1),PopDec(:,1:M-1)],2)).*[ones(N,1),1-PopDec(:,M-1:-1:1)]; + end + %% Generate points on the Pareto front + function R = GetOptimum(obj,N) + R = UniformPoint(N,obj.M); + end + %% Generate the image of Pareto front + function R = GetPF(obj) + if obj.M == 2 + R = obj.GetOptimum(100); + elseif obj.M == 3 + a = linspace(0,1,10)'; + R = {a*a',a*(1-a'),(1-a)*ones(size(a'))}; + else + R = []; + end + end + end +end + +function f = Rastrigin(x) + f = sum(x.^2-10.*cos(2.*pi.*x)+10,2); +end + +function f = Rosenbrock(x) + f = sum(100.*(x(:,1:size(x,2)-1).^2-x(:,2:size(x,2))).^2+(x(:,1:size(x,2)-1)-1).^2,2); +end \ No newline at end of file diff --git a/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP4.m b/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP4.m new file mode 100644 index 000000000..61e6effc8 --- /dev/null +++ b/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP4.m @@ -0,0 +1,91 @@ +classdef GLSMOP4 < PROBLEM +% +% General large-scale benchmark MOP +% nk --- 5 --- Number of subcomponents in each variable group + +%------------------------------- Reference -------------------------------- +% H. Gu, H. Wang, and J. Ma, Test suites and performance of algorithms in +% large-scale multiobjective evolutionary optimization, Proceedings of the +% IEEE Congress on Evolutionary Computation, 2024. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Haoran Gu + + properties(Access = private) + nk = 5; % Number of subcomponents in each variable group + sublen; % Number of variables in each subcomponent + len; % Cumulative sum of lengths of variable groups + end + methods + %% Default settings of the problem + function Setting(obj) + % Parameter setting + obj.nk = obj.ParameterSet(5); + if isempty(obj.M); obj.M = 3; end + if isempty(obj.D); obj.D = 100*obj.M; end + obj.lower = zeros(1,obj.D); + obj.upper = [ones(1,obj.M-1),10.*ones(1,obj.D-obj.M+1)]; + obj.encoding = ones(1,obj.D); + % Calculate the number of variables in each subcomponent + c = 3.8*0.1*(1-0.1); + for i = 1 : obj.M-1 + c = [c,3.8.*c(end).*(1-c(end))]; + end + obj.sublen = floor(c./sum(c).*(obj.D-obj.M+1)/obj.nk); + obj.len = [0,cumsum(obj.sublen*obj.nk)]; + end + %% Calculate objective values + function PopObj = CalObj(obj,PopDec) + [N,D] = size(PopDec); + M = obj.M; + %% Change PS by the transfer function + for i = M : 2 : D + PopDec(:,i) = obj.lower(i) + obj.upper(i)-PopDec(:,i); + end + PopDec(:,M:D) = (1+repmat((M:D)./D,N,1)).*PopDec(:,M:D) - repmat(PopDec(:,1)*10,1,D-M+1); + G = zeros(N,M); + for i = 1 : 2 : M + for j = 1 : obj.nk + G(:,i) = G(:,i) + Ackley(PopDec(:,obj.len(i)+M-1+(j-1)*obj.sublen(i)+1:obj.len(i)+M-1+j*obj.sublen(i))); + end + end + for i = 2 : 2 : M + for j = 1 : obj.nk + G(:,i) = G(:,i) + Griewank(PopDec(:,obj.len(i)+M-1+(j-1)*obj.sublen(i)+1:obj.len(i)+M-1+j*obj.sublen(i))); + end + end + G = G./repmat(obj.sublen,N,1)./obj.nk; + PopObj = (1+G).*fliplr(cumprod([ones(N,1),PopDec(:,1:M-1)],2)).*[ones(N,1),1-PopDec(:,M-1:-1:1)]; + end + %% Generate points on the Pareto front + function R = GetOptimum(obj,N) + R = UniformPoint(N,obj.M); + end + %% Generate the image of Pareto front + function R = GetPF(obj) + if obj.M == 2 + R = obj.GetOptimum(100); + elseif obj.M == 3 + a = linspace(0,1,10)'; + R = {a*a',a*(1-a'),(1-a)*ones(size(a'))}; + else + R = []; + end + end + end +end + +function f = Ackley(x) + f = 20-20.*exp(-0.2.*sqrt(sum(x.^2,2)./size(x,2)))-exp(sum(cos(2.*pi.*x),2)./size(x,2))+exp(1); +end + +function f = Griewank(x) + f = sum(x.^2,2)./4000 - prod(cos(x./repmat(sqrt(1:size(x,2)),size(x,1),1)),2) + 1; +end \ No newline at end of file diff --git a/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP5.m b/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP5.m new file mode 100644 index 000000000..a4310d325 --- /dev/null +++ b/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP5.m @@ -0,0 +1,88 @@ +classdef GLSMOP5 < PROBLEM +% +% General large-scale benchmark MOP +% nk --- 5 --- Number of subcomponents in each variable group + +%------------------------------- Reference -------------------------------- +% H. Gu, H. Wang, and J. Ma, Test suites and performance of algorithms in +% large-scale multiobjective evolutionary optimization, Proceedings of the +% IEEE Congress on Evolutionary Computation, 2024. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Haoran Gu + + properties(Access = private) + nk = 5; % Number of subcomponents in each variable group + sublen; % Number of variables in each subcomponent + len; % Cumulative sum of lengths of variable groups + end + methods + %% Default settings of the problem + function Setting(obj) + % Parameter setting + obj.nk = obj.ParameterSet(5); + if isempty(obj.M); obj.M = 3; end + if isempty(obj.D); obj.D = 100*obj.M; end + obj.lower = zeros(1,obj.D); + obj.upper = [ones(1,obj.M-1),10.*ones(1,obj.D-obj.M+1)]; + obj.encoding = ones(1,obj.D); + % Calculate the number of variables in each subcomponent + c = 3.8*0.1*(1-0.1); + for i = 1 : obj.M-1 + c = [c,3.8.*c(end).*(1-c(end))]; + end + obj.sublen = floor(c./sum(c).*(obj.D-obj.M+1)/obj.nk); + obj.len = [0,cumsum(obj.sublen*obj.nk)]; + end + %% Calculate objective values + function PopObj = CalObj(obj,PopDec) + [N,D] = size(PopDec); + M = obj.M; + %% Change PS by the transfer function + for i = M : 2 : D + PopDec(:,i) = obj.lower(i) + obj.upper(i)-PopDec(:,i); + end + PopDec(:,M:D) = (1+repmat(cos((M:D)./D*pi/2),N,1)).*PopDec(:,M:D) - repmat(PopDec(:,1)*10,1,D-M+1); + G = zeros(N,M); + for i = 1 : 2 : M + for j = 1 : obj.nk + G(:,i) = G(:,i) + Sphere(PopDec(:,obj.len(i)+M-1+(j-1)*obj.sublen(i)+1:obj.len(i)+M-1+j*obj.sublen(i))); + end + end + for i = 2 : 2 : M + for j = 1 : obj.nk + G(:,i) = G(:,i) + Sphere(PopDec(:,obj.len(i)+M-1+(j-1)*obj.sublen(i)+1:obj.len(i)+M-1+j*obj.sublen(i))); + end + end + G = G./repmat(obj.sublen,N,1)./obj.nk; + PopObj = (1+G+[G(:,2:end),zeros(N,1)]).*fliplr(cumprod([ones(N,1),cos(PopDec(:,1:M-1)*pi/2)],2)).*[ones(N,1),sin(PopDec(:,M-1:-1:1)*pi/2)]; + end + %% Generate points on the Pareto front + function R = GetOptimum(obj,N) + R = UniformPoint(N,obj.M); + R = R./repmat(sqrt(sum(R.^2,2)),1,obj.M); + end + %% Generate the image of Pareto front + function R = GetPF(obj) + if obj.M == 2 + R = obj.GetOptimum(100); + elseif obj.M == 3 + a = linspace(0,pi/2,10)'; + R = {sin(a)*cos(a'),sin(a)*sin(a'),cos(a)*ones(size(a'))}; + else + R = []; + end + end + end +end + +function f = Sphere(x) + f = sum(x.^2,2); +end \ No newline at end of file diff --git a/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP6.m b/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP6.m new file mode 100644 index 000000000..f03a812ce --- /dev/null +++ b/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP6.m @@ -0,0 +1,92 @@ +classdef GLSMOP6 < PROBLEM +% +% General large-scale benchmark MOP +% nk --- 5 --- Number of subcomponents in each variable group + +%------------------------------- Reference -------------------------------- +% H. Gu, H. Wang, and J. Ma, Test suites and performance of algorithms in +% large-scale multiobjective evolutionary optimization, Proceedings of the +% IEEE Congress on Evolutionary Computation, 2024. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Haoran Gu + + properties(Access = private) + nk = 5; % Number of subcomponents in each variable group + sublen; % Number of variables in each subcomponent + len; % Cumulative sum of lengths of variable groups + end + methods + %% Default settings of the problem + function Setting(obj) + % Parameter setting + obj.nk = obj.ParameterSet(5); + if isempty(obj.M); obj.M = 3; end + if isempty(obj.D); obj.D = 100*obj.M; end + obj.lower = zeros(1,obj.D); + obj.upper = [ones(1,obj.M-1),10.*ones(1,obj.D-obj.M+1)]; + obj.encoding = ones(1,obj.D); + % Calculate the number of variables in each subcomponent + c = 3.8*0.1*(1-0.1); + for i = 1 : obj.M-1 + c = [c,3.8.*c(end).*(1-c(end))]; + end + obj.sublen = floor(c./sum(c).*(obj.D-obj.M+1)/obj.nk); + obj.len = [0,cumsum(obj.sublen*obj.nk)]; + end + %% Calculate objective values + function PopObj = CalObj(obj,PopDec) + [N,D] = size(PopDec); + M = obj.M; + %% Change PS by the transfer function + for i = M : 2 : D + PopDec(:,i) = obj.lower(i) + obj.upper(i)-PopDec(:,i); + end + PopDec(:,M:D) = (1+repmat(cos((M:D)./D*pi/2),N,1)).*PopDec(:,M:D) - repmat(PopDec(:,1)*10,1,D-M+1); + G = zeros(N,M); + for i = 1 : 2 : M + for j = 1 : obj.nk + G(:,i) = G(:,i) + Rosenbrock(PopDec(:,obj.len(i)+M-1+(j-1)*obj.sublen(i)+1:obj.len(i)+M-1+j*obj.sublen(i))); + end + end + for i = 2 : 2 : M + for j = 1 : obj.nk + G(:,i) = G(:,i) + Schwefel(PopDec(:,obj.len(i)+M-1+(j-1)*obj.sublen(i)+1:obj.len(i)+M-1+j*obj.sublen(i))); + end + end + G = G./repmat(obj.sublen,N,1)./obj.nk; + PopObj = (1+G+[G(:,2:end),zeros(N,1)]).*fliplr(cumprod([ones(N,1),cos(PopDec(:,1:M-1)*pi/2)],2)).*[ones(N,1),sin(PopDec(:,M-1:-1:1)*pi/2)]; + end + %% Generate points on the Pareto front + function R = GetOptimum(obj,N) + R = UniformPoint(N,obj.M); + R = R./repmat(sqrt(sum(R.^2,2)),1,obj.M); + end + %% Generate the image of Pareto front + function R = GetPF(obj) + if obj.M == 2 + R = obj.GetOptimum(100); + elseif obj.M == 3 + a = linspace(0,pi/2,10)'; + R = {sin(a)*cos(a'),sin(a)*sin(a'),cos(a)*ones(size(a'))}; + else + R = []; + end + end + end +end + +function f = Rosenbrock(x) + f = sum(100.*(x(:,1:size(x,2)-1).^2-x(:,2:size(x,2))).^2+(x(:,1:size(x,2)-1)-1).^2,2); +end + +function f = Schwefel(x) + f = max(abs(x),[],2); +end \ No newline at end of file diff --git a/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP7.m b/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP7.m new file mode 100644 index 000000000..38d140a24 --- /dev/null +++ b/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP7.m @@ -0,0 +1,92 @@ +classdef GLSMOP7 < PROBLEM +% +% General large-scale benchmark MOP +% nk --- 5 --- Number of subcomponents in each variable group + +%------------------------------- Reference -------------------------------- +% H. Gu, H. Wang, and J. Ma, Test suites and performance of algorithms in +% large-scale multiobjective evolutionary optimization, Proceedings of the +% IEEE Congress on Evolutionary Computation, 2024. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Haoran Gu + + properties(Access = private) + nk = 5; % Number of subcomponents in each variable group + sublen; % Number of variables in each subcomponent + len; % Cumulative sum of lengths of variable groups + end + methods + %% Default settings of the problem + function Setting(obj) + % Parameter setting + obj.nk = obj.ParameterSet(5); + if isempty(obj.M); obj.M = 3; end + if isempty(obj.D); obj.D = 100*obj.M; end + obj.lower = zeros(1,obj.D); + obj.upper = [ones(1,obj.M-1),10.*ones(1,obj.D-obj.M+1)]; + obj.encoding = ones(1,obj.D); + % Calculate the number of variables in each subcomponent + c = 3.8*0.1*(1-0.1); + for i = 1 : obj.M-1 + c = [c,3.8.*c(end).*(1-c(end))]; + end + obj.sublen = floor(c./sum(c).*(obj.D-obj.M+1)/obj.nk); + obj.len = [0,cumsum(obj.sublen*obj.nk)]; + end + %% Calculate objective values + function PopObj = CalObj(obj,PopDec) + [N,D] = size(PopDec); + M = obj.M; + %% Change PS by the transfer function + for i = M : 2 : D + PopDec(:,i) = obj.lower(i) + obj.upper(i)-PopDec(:,i); + end + PopDec(:,M:D) = (1+repmat(cos((M:D)./D*pi/2),N,1)).*PopDec(:,M:D) - repmat(PopDec(:,1)*10,1,D-M+1); + G = zeros(N,M); + for i = 1 : 2 : M + for j = 1 : obj.nk + G(:,i) = G(:,i) + Ackley(PopDec(:,obj.len(i)+M-1+(j-1)*obj.sublen(i)+1:obj.len(i)+M-1+j*obj.sublen(i))); + end + end + for i = 2 : 2 : M + for j = 1 : obj.nk + G(:,i) = G(:,i) + Rosenbrock(PopDec(:,obj.len(i)+M-1+(j-1)*obj.sublen(i)+1:obj.len(i)+M-1+j*obj.sublen(i))); + end + end + G = G./repmat(obj.sublen,N,1)./obj.nk; + PopObj = (1+G+[G(:,2:end),zeros(N,1)]).*fliplr(cumprod([ones(N,1),cos(PopDec(:,1:M-1)*pi/2)],2)).*[ones(N,1),sin(PopDec(:,M-1:-1:1)*pi/2)]; + end + %% Generate points on the Pareto front + function R = GetOptimum(obj,N) + R = UniformPoint(N,obj.M); + R = R./repmat(sqrt(sum(R.^2,2)),1,obj.M); + end + %% Generate the image of Pareto front + function R = GetPF(obj) + if obj.M == 2 + R = obj.GetOptimum(100); + elseif obj.M == 3 + a = linspace(0,pi/2,10)'; + R = {sin(a)*cos(a'),sin(a)*sin(a'),cos(a)*ones(size(a'))}; + else + R = []; + end + end + end +end + +function f = Ackley(x) + f = 20-20.*exp(-0.2.*sqrt(sum(x.^2,2)./size(x,2)))-exp(sum(cos(2.*pi.*x),2)./size(x,2))+exp(1); +end + +function f = Rosenbrock(x) + f = sum(100.*(x(:,1:size(x,2)-1).^2-x(:,2:size(x,2))).^2+(x(:,1:size(x,2)-1)-1).^2,2); +end \ No newline at end of file diff --git a/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP8.m b/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP8.m new file mode 100644 index 000000000..7dc02540d --- /dev/null +++ b/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP8.m @@ -0,0 +1,92 @@ +classdef GLSMOP8 < PROBLEM +% +% General large-scale benchmark MOP +% nk --- 5 --- Number of subcomponents in each variable group + +%------------------------------- Reference -------------------------------- +% H. Gu, H. Wang, and J. Ma, Test suites and performance of algorithms in +% large-scale multiobjective evolutionary optimization, Proceedings of the +% IEEE Congress on Evolutionary Computation, 2024. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Haoran Gu + + properties(Access = private) + nk = 5; % Number of subcomponents in each variable group + sublen; % Number of variables in each subcomponent + len; % Cumulative sum of lengths of variable groups + end + methods + %% Default settings of the problem + function Setting(obj) + % Parameter setting + obj.nk = obj.ParameterSet(5); + if isempty(obj.M); obj.M = 3; end + if isempty(obj.D); obj.D = 100*obj.M; end + obj.lower = zeros(1,obj.D); + obj.upper = [ones(1,obj.M-1),10.*ones(1,obj.D-obj.M+1)]; + obj.encoding = ones(1,obj.D); + % Calculate the number of variables in each subcomponent + c = 3.8*0.1*(1-0.1); + for i = 1 : obj.M-1 + c = [c,3.8.*c(end).*(1-c(end))]; + end + obj.sublen = floor(c./sum(c).*(obj.D-obj.M+1)/obj.nk); + obj.len = [0,cumsum(obj.sublen*obj.nk)]; + end + %% Calculate objective values + function PopObj = CalObj(obj,PopDec) + [N,D] = size(PopDec); + M = obj.M; + %% Change PS by the transfer function + for i = M : 2 : D + PopDec(:,i) = obj.lower(i) + obj.upper(i)-PopDec(:,i); + end + PopDec(:,M:D) = (1+repmat(cos((M:D)./D*pi/2),N,1)).*PopDec(:,M:D) - repmat(PopDec(:,1)*10,1,D-M+1); + G = zeros(N,M); + for i = 1 : 2 : M + for j = 1 : obj.nk + G(:,i) = G(:,i) + Griewank(PopDec(:,obj.len(i)+M-1+(j-1)*obj.sublen(i)+1:obj.len(i)+M-1+j*obj.sublen(i))); + end + end + for i = 2 : 2 : M + for j = 1 : obj.nk + G(:,i) = G(:,i) + Sphere(PopDec(:,obj.len(i)+M-1+(j-1)*obj.sublen(i)+1:obj.len(i)+M-1+j*obj.sublen(i))); + end + end + G = G./repmat(obj.sublen,N,1)./obj.nk; + PopObj = (1+G+[G(:,2:end),zeros(N,1)]).*fliplr(cumprod([ones(N,1),cos(PopDec(:,1:M-1)*pi/2)],2)).*[ones(N,1),sin(PopDec(:,M-1:-1:1)*pi/2)]; + end + %% Generate points on the Pareto front + function R = GetOptimum(obj,N) + R = UniformPoint(N,obj.M); + R = R./repmat(sqrt(sum(R.^2,2)),1,obj.M); + end + %% Generate the image of Pareto front + function R = GetPF(obj) + if obj.M == 2 + R = obj.GetOptimum(100); + elseif obj.M == 3 + a = linspace(0,pi/2,10)'; + R = {sin(a)*cos(a'),sin(a)*sin(a'),cos(a)*ones(size(a'))}; + else + R = []; + end + end + end +end + +function f = Griewank(x) + f = sum(x.^2,2)./4000 - prod(cos(x./repmat(sqrt(1:size(x,2)),size(x,1),1)),2) + 1; +end + +function f = Sphere(x) + f = sum(x.^2,2); +end \ No newline at end of file diff --git a/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP9.m b/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP9.m new file mode 100644 index 000000000..7de0e4532 --- /dev/null +++ b/PlatEMO/Problems/Multi-objective optimization/GLSMOP/GLSMOP9.m @@ -0,0 +1,104 @@ +classdef GLSMOP9 < PROBLEM +% +% General large-scale benchmark MOP +% nk --- 5 --- Number of subcomponents in each variable group + +%------------------------------- Reference -------------------------------- +% H. Gu, H. Wang, and J. Ma, Test suites and performance of algorithms in +% large-scale multiobjective evolutionary optimization, Proceedings of the +% IEEE Congress on Evolutionary Computation, 2024. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Haoran Gu + + properties(Access = private) + nk = 5; % Number of subcomponents in each variable group + sublen; % Number of variables in each subcomponent + len; % Cumulative sum of lengths of variable groups + end + methods + %% Default settings of the problem + function Setting(obj) + % Parameter setting + obj.nk = obj.ParameterSet(5); + if isempty(obj.M); obj.M = 3; end + if isempty(obj.D); obj.D = 100*obj.M; end + obj.lower = zeros(1,obj.D); + obj.upper = [ones(1,obj.M-1),10.*ones(1,obj.D-obj.M+1)]; + obj.encoding = ones(1,obj.D); + % Calculate the number of variables in each subcomponent + c = 3.8*0.1*(1-0.1); + for i = 1 : obj.M-1 + c = [c,3.8.*c(end).*(1-c(end))]; + end + obj.sublen = floor(c./sum(c).*(obj.D-obj.M+1)/obj.nk); + obj.len = [0,cumsum(obj.sublen*obj.nk)]; + end + %% Calculate objective values + function PopObj = CalObj(obj,PopDec) + [N,D] = size(PopDec); + M = obj.M; + %% Change PS by the transfer function + for i = M : 2 : D + PopDec(:,i) = obj.lower(i) + obj.upper(i)-PopDec(:,i); + end + PopDec(:,M:D) = (1+repmat(cos((M:D)./D*pi/2),N,1)).*PopDec(:,M:D) - repmat(PopDec(:,1)*10,1,D-M+1); + G = zeros(N,M); + for i = 1 : 2 : M + for j = 1 : obj.nk + G(:,i) = G(:,i) + Sphere(PopDec(:,obj.len(i)+M-1+(j-1)*obj.sublen(i)+1:obj.len(i)+M-1+j*obj.sublen(i))); + end + end + for i = 2 : 2 : M + for j = 1 : obj.nk + G(:,i) = G(:,i) + Ackley(PopDec(:,obj.len(i)+M-1+(j-1)*obj.sublen(i)+1:obj.len(i)+M-1+j*obj.sublen(i))); + end + end + G = 1 + sum(G./repmat(obj.sublen,N,1)./obj.nk,2); + PopObj(:,1:M-1) = PopDec(:,1:M-1); + PopObj(:,M) = (1+G).*(M-sum(PopObj(:,1:M-1)./(1+repmat(G,1,M-1)).*(1+sin(3*pi.*PopObj(:,1:M-1))),2)); + end + %% Generate points on the Pareto front + function R = GetOptimum(obj,N) + interval = [0,0.251412,0.631627,0.859401]; + median = (interval(2)-interval(1))/(interval(4)-interval(3)+interval(2)-interval(1)); + X = UniformPoint(N,obj.M-1,'grid'); + X(X<=median) = X(X<=median)*(interval(2)-interval(1))/median+interval(1); + X(X>median) = (X(X>median)-median)*(interval(4)-interval(3))/(1-median)+interval(3); + R = [X,2*(obj.M-sum(X/2.*(1+sin(3*pi.*X)),2))]; + end + %% Generate the image of Pareto front + function R = GetPF(obj) + if obj.M == 2 + x = linspace(0,1,100)'; + y = 2*(2-x/2.*(1+sin(3*pi*x))); + nd = NDSort([x,y],1)==1; + x(~nd) = nan; + R = [x,y]; + elseif obj.M == 3 + [x,y] = meshgrid(linspace(0,1,20)); + z = 2*(3-x/2.*(1+sin(3*pi*x))-y/2.*(1+sin(3*pi*y))); + nd = reshape(NDSort([x(:),y(:),z(:)],1)==1,size(z)); + z(~nd) = nan; + R = {x,y,z}; + else + R = []; + end + end + end +end + +function f = Sphere(x) + f = sum(x.^2,2); +end + +function f = Ackley(x) + f = 20-20.*exp(-0.2.*sqrt(sum(x.^2,2)./size(x,2)))-exp(sum(cos(2.*pi.*x),2)./size(x,2))+exp(1); +end \ No newline at end of file diff --git a/PlatEMO/Problems/Multi-objective optimization/LRMOP/LRMOP1.m b/PlatEMO/Problems/Multi-objective optimization/LRMOP/LRMOP1.m new file mode 100644 index 000000000..3154e1e58 --- /dev/null +++ b/PlatEMO/Problems/Multi-objective optimization/LRMOP/LRMOP1.m @@ -0,0 +1,83 @@ +classdef LRMOP1 < PROBLEM +% +% Sparse robust multi-objective optimization problem +% theta --- 0.1 --- Sparsity of the Pareto set +% H --- 50 --- Number of disturbances + +%------------------------------- Reference -------------------------------- +% S. Shao, Y. Tian, L. Zhang, K. C. Tan, and X. Zhang, An evolutionary +% algorithm for solving large-scale robust multi-objective optimization +% problems, IEEE Transactions on Evolutionary Computation, 2024. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + properties + theta = 0.1; % Sparsity of the Pareto set + H = 50; % Number of disturbances + end + methods + %% Default settings of the problem + function Setting(obj) + [obj.theta,obj.H] = obj.ParameterSet(0.1,50); + if isempty(obj.M); obj.M = 2; end + if isempty(obj.D); obj.D = 100; end + obj.lower = [zeros(1,obj.M-1)+0,zeros(1,obj.D-obj.M+1)-1]; + obj.upper = [zeros(1,obj.M-1)+1,zeros(1,obj.D-obj.M+1)+2]; + obj.encoding = ones(1,obj.D); + end + %% Calculate objective values + function PopObj = CalObj(obj,X) + k = ceil(obj.theta*(obj.D-obj.M+1)); + K = k+round(k*rand()); + a= random('Normal',1,0.2); + g = sum(g1(X(:,2:end),a).*g2(X(:,2:end),0),2) + abs(K-sum(X(:,2:end)~=0,2)); + PopObj = repmat(1+g/(obj.D-obj.M+1),1,obj.M).*fliplr(cumprod([ones(size(X,1),1),X(:,1:obj.M-1)],2)).*[ones(size(X,1),1),1-X(:,obj.M-1:-1:1)]; + end + %% Generate points on the Pareto front + function R = GetOptimum(obj,N) + R = UniformPoint(N,obj.M); + end + %% Generate the image of Pareto front + function R = GetPF(obj) + if obj.M == 2 + R = obj.GetOptimum(100); + elseif obj.M == 3 + a = linspace(0,1,10)'; + R = {a*a',a*(1-a'),(1-a)*ones(size(a'))}; + else + R = []; + end + end + %% Calculate the metric value + function score = CalMetric(obj,metName,Population) + switch metName + case {'Mean_IGD','Mean_HV','Worst_IGD','Worst_HV'} + score = feval(metName,Population,obj); + otherwise + score = feval(metName,Population,obj.optimum); + end + end + %% Perturb solutions multiple times + function PopX = Perturb(obj,PopDec,N) + if nargin < 3; N = obj.H; end + PopX = []; + for i = 1 : N + PopX = [PopX,SOLUTION(PopDec,obj.CalObj(PopDec),obj.CalCon(PopDec))]; + end + end + end +end + +function g = g1(x,t) + g = (3+x-4*t*t*t).^2; +end + +function g = g2(x,t) + g = 4-(x-t)-4./exp(100*(x-t).^2); +end \ No newline at end of file diff --git a/PlatEMO/Problems/Multi-objective optimization/LRMOP/LRMOP2.m b/PlatEMO/Problems/Multi-objective optimization/LRMOP/LRMOP2.m new file mode 100644 index 000000000..c9b167001 --- /dev/null +++ b/PlatEMO/Problems/Multi-objective optimization/LRMOP/LRMOP2.m @@ -0,0 +1,92 @@ +classdef LRMOP2 < PROBLEM +% +% Sparse robust multi-objective optimization problem +% theta --- 0.1 --- Sparsity of the Pareto set +% H --- 50 --- Number of disturbances + +%------------------------------- Reference -------------------------------- +% S. Shao, Y. Tian, L. Zhang, K. C. Tan, and X. Zhang, An evolutionary +% algorithm for solving large-scale robust multi-objective optimization +% problems, IEEE Transactions on Evolutionary Computation, 2024. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + properties + theta = 0.1; % Sparsity of the Pareto set + H = 50; % Number of disturbances + end + methods + %% Default settings of the problem + function Setting(obj) + [obj.theta,obj.H] = obj.ParameterSet(0.1,50); + if isempty(obj.M); obj.M = 2; end + if isempty(obj.D); obj.D = 100; end + obj.lower = [zeros(1,obj.M-1)+0,zeros(1,obj.D-obj.M+1)-1]; + obj.upper = [zeros(1,obj.M-1)+1,zeros(1,obj.D-obj.M+1)+2]; + obj.encoding = ones(1,obj.D); + end + %% Calculate objective values + function PopObj = CalObj(obj,X) + [N,D] = size(X); + M = obj.M; + K = ceil(obj.theta*(D-M+1)); + a= random('Normal',1,0.2); + g = sum(g1(X(:,2:end),a).*g3(X(:,2:end),0),2) + abs(K-sum(X(:,2:end)~=0,2)); + PopObj = repmat(1+g/(D-M+1),1,M).*fliplr(cumprod([ones(N,1),1-cos(X(:,1:M-1)*pi/2)],2)).*[ones(N,1),1-sin(X(:,M-1:-1:1)*pi/2)]; + end + %% Generate points on the Pareto front + function R = GetOptimum(obj,N) + R = UniformPoint(N,obj.M); + c = ones(size(R,1),obj.M); + for i = 1 : size(R,1) + for j = 2 : obj.M + temp = R(i,j)/R(i,1)*prod(1-c(i,obj.M-j+2:obj.M-1)); + c(i,obj.M-j+1) = (temp^2-temp+sqrt(2*temp))/(temp^2+1); + end + end + x = acos(c)*2/pi; + R = fliplr(cumprod([ones(size(x,1),1),1-cos(x(:,1:end-1)*pi/2)],2)).*[ones(size(x,1),1),1-sin(x(:,end-1:-1:1)*pi/2)]; + end + %% Generate the image of Pareto front + function R = GetPF(obj) + if obj.M == 2 + R = obj.GetOptimum(100); + elseif obj.M == 3 + a = linspace(0,pi/2,10)'; + R = {(1-cos(a))*(1-cos(a')),(1-cos(a))*(1-sin(a')),(1-sin(a))*ones(size(a'))}; + else + R = []; + end + end + %% Calculate the metric value + function score = CalMetric(obj,metName,Population) + switch metName + case {'Mean_IGD','Mean_HV','Worst_IGD','Worst_HV'} + score = feval(metName,Population,obj); + otherwise + score = feval(metName,Population,obj.optimum); + end + end + %% Perturb solutions multiple times + function PopX = Perturb(obj,PopDec,N) + if nargin < 3; N = obj.H; end + PopX = []; + for i = 1 : N + PopX = [PopX,SOLUTION(PopDec,obj.CalObj(PopDec),obj.CalCon(PopDec))]; + end + end + end +end +function g = g1(x,t) + g = (3+x-4*t*t*t).^2; +end + +function g = g3(x,t) + g = 4-(x-t)-4./exp(100*(x-t).^2); +end \ No newline at end of file diff --git a/PlatEMO/Problems/Multi-objective optimization/LRMOP/LRMOP3.m b/PlatEMO/Problems/Multi-objective optimization/LRMOP/LRMOP3.m new file mode 100644 index 000000000..53a6dcf4c --- /dev/null +++ b/PlatEMO/Problems/Multi-objective optimization/LRMOP/LRMOP3.m @@ -0,0 +1,92 @@ +classdef LRMOP3 < PROBLEM +% +% Sparse robust multi-objective optimization problem +% theta --- 0.1 --- Sparsity of the Pareto set +% H --- 50 --- Number of disturbances + +%------------------------------- Reference -------------------------------- +% S. Shao, Y. Tian, L. Zhang, K. C. Tan, and X. Zhang, An evolutionary +% algorithm for solving large-scale robust multi-objective optimization +% problems, IEEE Transactions on Evolutionary Computation, 2024. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + properties + theta = 0.1; % Sparsity of the Pareto set + H = 50; % Number of disturbances + end + methods + %% Default settings of the problem + function Setting(obj) + [obj.theta,obj.H] = obj.ParameterSet(0.1,50); + if isempty(obj.M); obj.M = 2; end + if isempty(obj.D); obj.D = 100; end + obj.lower = [zeros(1,obj.M-1)+0,zeros(1,obj.D-obj.M+1)-1]; + obj.upper = [zeros(1,obj.M-1)+1,zeros(1,obj.D-obj.M+1)+2]; + obj.encoding = ones(1,obj.D); + end + %% Calculate objective values + function PopObj = CalObj(obj,X) + [N,D] = size(X); + M = obj.M; + K = ceil(obj.theta*(D-M+1)); + a= random('Normal',1,0.2); + g = sum(g1(X(:,2:end),a).*g5(X(:,2:end),0),2) + abs(K-sum(X(:,2:end)~=0,2)); + PopObj = repmat(1+g/(D-M+1),1,M).*fliplr(cumprod([ones(N,1),1-cos(X(:,1:M-1)*pi/2)],2)).*[ones(N,1),1-sin(X(:,M-1:-1:1)*pi/2)]; + end + %% Generate points on the Pareto front + function R = GetOptimum(obj,N) + R = UniformPoint(N,obj.M); + c = ones(size(R,1),obj.M); + for i = 1 : size(R,1) + for j = 2 : obj.M + temp = R(i,j)/R(i,1)*prod(1-c(i,obj.M-j+2:obj.M-1)); + c(i,obj.M-j+1) = (temp^2-temp+sqrt(2*temp))/(temp^2+1); + end + end + x = acos(c)*2/pi; + R = fliplr(cumprod([ones(size(x,1),1),1-cos(x(:,1:end-1)*pi/2)],2)).*[ones(size(x,1),1),1-sin(x(:,end-1:-1:1)*pi/2)]; + end + %% Generate the image of Pareto front + function R = GetPF(obj) + if obj.M == 2 + R = obj.GetOptimum(100); + elseif obj.M == 3 + a = linspace(0,pi/2,10)'; + R = {(1-cos(a))*(1-cos(a')),(1-cos(a))*(1-sin(a')),(1-sin(a))*ones(size(a'))}; + else + R = []; + end + end + %% Calculate the metric value + function score = CalMetric(obj,metName,Population) + switch metName + case {'Mean_IGD','Mean_HV','Worst_IGD','Worst_HV'} + score = feval(metName,Population,obj); + otherwise + score = feval(metName,Population,obj.optimum); + end + end + %% Perturb solutions multiple times + function PopX = Perturb(obj,PopDec,N) + if nargin < 3; N = obj.H; end + PopX = []; + for i = 1 : N + PopX = [PopX,SOLUTION(PopDec,obj.CalObj(PopDec),obj.CalCon(PopDec))]; + end + end + end +end +function g = g1(x,t) + g = (3+x-4*t*t*t).^2; +end + +function g = g5(x,t) + g = exp(log(2)*((x-t).^2)).*(sin(6*pi*(x-t)).^2)+(x-t).^2; +end \ No newline at end of file diff --git a/PlatEMO/Problems/Multi-objective optimization/LRMOP/LRMOP4.m b/PlatEMO/Problems/Multi-objective optimization/LRMOP/LRMOP4.m new file mode 100644 index 000000000..883a4576a --- /dev/null +++ b/PlatEMO/Problems/Multi-objective optimization/LRMOP/LRMOP4.m @@ -0,0 +1,94 @@ +classdef LRMOP4 < PROBLEM +% +% Sparse robust multi-objective optimization problem +% theta --- 0.1 --- Sparsity of the Pareto set +% H --- 50 --- Number of disturbances + +%------------------------------- Reference -------------------------------- +% S. Shao, Y. Tian, L. Zhang, K. C. Tan, and X. Zhang, An evolutionary +% algorithm for solving large-scale robust multi-objective optimization +% problems, IEEE Transactions on Evolutionary Computation, 2024. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + properties + theta = 0.1; % Sparsity of the Pareto set + H = 50; % Number of disturbances + end + methods + %% Default settings of the problem + function Setting(obj) + [obj.theta,obj.H] = obj.ParameterSet(0.1,50); + if isempty(obj.M); obj.M = 2; end + if isempty(obj.D); obj.D = 100; end + obj.lower = [zeros(1,obj.M-1)+0,zeros(1,obj.D-obj.M+1)-1]; + obj.upper = [zeros(1,obj.M-1)+1,zeros(1,obj.D-obj.M+1)+2]; + obj.encoding = ones(1,obj.D); + end + %% Calculate objective values + function PopObj = CalObj(obj,X) + [N,~] = size(X); + M = obj.M; + k = ceil(obj.theta*(obj.D-obj.M+1)); + K = k+round(k*rand()); + a= random('Normal',1,0.2); + g = sum(g1(X(:,2:end),a).*g5(X(:,2:end),0),2) + abs(K-sum(X(:,2:end)~=0,2)); + PopObj = repmat(1+g/(obj.D-obj.M+1),1,obj.M).*fliplr(cumprod([ones(N,1),1-cos(X(:,1:M-1)*pi/2)],2)).*[ones(N,1),1-sin(X(:,M-1:-1:1)*pi/2)]; + end + %% Generate points on the Pareto front + function R = GetOptimum(obj,N) + R = UniformPoint(N,obj.M); + c = ones(size(R,1),obj.M); + for i = 1 : size(R,1) + for j = 2 : obj.M + temp = R(i,j)/R(i,1)*prod(1-c(i,obj.M-j+2:obj.M-1)); + c(i,obj.M-j+1) = (temp^2-temp+sqrt(2*temp))/(temp^2+1); + end + end + x = acos(c)*2/pi; + R = fliplr(cumprod([ones(size(x,1),1),1-cos(x(:,1:end-1)*pi/2)],2)).*[ones(size(x,1),1),1-sin(x(:,end-1:-1:1)*pi/2)]; + end + %% Generate the image of Pareto front + function R = GetPF(obj) + if obj.M == 2 + R = obj.GetOptimum(100); + elseif obj.M == 3 + a = linspace(0,pi/2,10)'; + R = {(1-cos(a))*(1-cos(a')),(1-cos(a))*(1-sin(a')),(1-sin(a))*ones(size(a'))}; + else + R = []; + end + end + %% Calculate the metric value + function score = CalMetric(obj,metName,Population) + switch metName + case {'Mean_IGD','Mean_HV','Worst_IGD','Worst_HV'} + score = feval(metName,Population,obj); + otherwise + score = feval(metName,Population,obj.optimum); + end + end + %% Perturb solutions multiple times + function PopX = Perturb(obj,PopDec,N) + if nargin < 3; N = obj.H; end + PopX = []; + for i = 1 : N + PopX = [PopX,SOLUTION(PopDec,obj.CalObj(PopDec),obj.CalCon(PopDec))]; + end + end + end +end + +function g = g1(x,t) + g = (3+x-4*t*t*t).^2; +end + +function g = g5(x,t) + g = exp(log(2)*((x-t).^2)).*(sin(6*pi*(x-t)).^2)+(x-t).^2; +end \ No newline at end of file diff --git a/PlatEMO/Problems/Multi-objective optimization/LRMOP/LRMOP5.m b/PlatEMO/Problems/Multi-objective optimization/LRMOP/LRMOP5.m new file mode 100644 index 000000000..c2a413814 --- /dev/null +++ b/PlatEMO/Problems/Multi-objective optimization/LRMOP/LRMOP5.m @@ -0,0 +1,85 @@ +classdef LRMOP5 < PROBLEM +% +% Sparse robust multi-objective optimization problem +% theta --- 0.1 --- Sparsity of the Pareto set +% H --- 50 --- Number of disturbances + +%------------------------------- Reference -------------------------------- +% S. Shao, Y. Tian, L. Zhang, K. C. Tan, and X. Zhang, An evolutionary +% algorithm for solving large-scale robust multi-objective optimization +% problems, IEEE Transactions on Evolutionary Computation, 2024. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + properties + theta = 0.1; % Sparsity of the Pareto set + H = 50; % Number of disturbances + end + methods + %% Default settings of the problem + function Setting(obj) + [obj.theta,obj.H] = obj.ParameterSet(0.1,50); + if isempty(obj.M); obj.M = 2; end + if isempty(obj.D); obj.D = 100; end + obj.lower = [zeros(1,obj.M-1)+0,zeros(1,obj.D-obj.M+1)-1]; + obj.upper = [zeros(1,obj.M-1)+1,zeros(1,obj.D-obj.M+1)+2]; + obj.encoding = ones(1,obj.D); + end + %% Calculate objective values + function PopObj = CalObj(obj,X) + [~,D] = size(X); + M = obj.M; + k = ceil(obj.theta*(obj.D-obj.M+1)); + K = k; + a= random('Normal',1,0.2); + g = sum(g1(X(:,2:end),a).*g2(X(:,2:end),0),2) + abs(K-sum(X(:,2:end)~=0,2)); + PopObj = repmat(1+g/(D-M+1),1,M).*fliplr(cumprod([ones(size(X,1),1),cos(X(:,1:obj.M-1)*pi/2)],2)).*[ones(size(X,1),1),sin(X(:,obj.M-1:-1:1)*pi/2)]; + end + %% Generate points on the Pareto front + function R = GetOptimum(obj,N) + R = UniformPoint(N,obj.M); + R = R./repmat(sqrt(sum(R.^2,2)),1,obj.M); + end + %% Generate the image of Pareto front + function R = GetPF(obj) + if obj.M == 2 + R = obj.GetOptimum(100); + elseif obj.M == 3 + a = linspace(0,pi/2,10)'; + R = {cos(a)*cos(a'),cos(a)*sin(a'),sin(a)*ones(size(a'))}; + else + R = []; + end + end + %% Calculate the metric value + function score = CalMetric(obj,metName,Population) + switch metName + case {'Mean_IGD','Mean_HV','Worst_IGD','Worst_HV'} + score = feval(metName,Population,obj); + otherwise + score = feval(metName,Population,obj.optimum); + end + end + %% Perturb solutions multiple times + function PopX = Perturb(obj,PopDec,N) + if nargin < 3; N = obj.H; end + PopX = []; + for i = 1 : N + PopX = [PopX,SOLUTION(PopDec,obj.CalObj(PopDec),obj.CalCon(PopDec))]; + end + end + end +end +function g = g2(x,t) + g = 4-(x-t)-4./exp(100*(x-t).^2); +end + +function g = g1(x,t) + g = (3+x-4*t*t*t).^2; +end \ No newline at end of file diff --git a/PlatEMO/Problems/Multi-objective optimization/LRMOP/LRMOP6.m b/PlatEMO/Problems/Multi-objective optimization/LRMOP/LRMOP6.m new file mode 100644 index 000000000..8ddb9320a --- /dev/null +++ b/PlatEMO/Problems/Multi-objective optimization/LRMOP/LRMOP6.m @@ -0,0 +1,83 @@ +classdef LRMOP6 < PROBLEM +% +% Sparse robust multi-objective optimization problem +% theta --- 0.1 --- Sparsity of the Pareto set +% H --- 50 --- Number of disturbances + +%------------------------------- Reference -------------------------------- +% S. Shao, Y. Tian, L. Zhang, K. C. Tan, and X. Zhang, An evolutionary +% algorithm for solving large-scale robust multi-objective optimization +% problems, IEEE Transactions on Evolutionary Computation, 2024. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + properties + theta = 0.1; % Sparsity of the Pareto set + H = 50; % Number of disturbances + end + methods + %% Default settings of the problem + function Setting(obj) + [obj.theta,obj.H] = obj.ParameterSet(0.1,50); + if isempty(obj.M); obj.M = 2; end + if isempty(obj.D); obj.D = 100; end + obj.lower = [zeros(1,obj.M-1)+0,zeros(1,obj.D-obj.M+1)-1]; + obj.upper = [zeros(1,obj.M-1)+1,zeros(1,obj.D-obj.M+1)+2]; + obj.encoding = ones(1,obj.D); + end + %% Calculate objective values + function PopObj = CalObj(obj,X) + k = ceil(obj.theta*(obj.D-obj.M+1)); + K = k; + a= random('Normal',1,0.2); + g = sum(g1(X(:,2:end),a).*g4(X(:,2:end),0),2) + abs(K-sum(X(:,2:end)~=0,2)); + PopObj = repmat(1+g/(obj.D-obj.M+1),1,obj.M).*fliplr(cumprod([ones(size(X,1),1),X(:,1:obj.M-1)],2)).*[ones(size(X,1),1),1-X(:,obj.M-1:-1:1)]; + end + %% Generate points on the Pareto front + function R = GetOptimum(obj,N) + R = UniformPoint(N,obj.M); + end + %% Generate the image of Pareto front + function R = GetPF(obj) + if obj.M == 2 + R = obj.GetOptimum(100); + elseif obj.M == 3 + a = linspace(0,1,10)'; + R = {a*a',a*(1-a'),(1-a)*ones(size(a'))}; + else + R = []; + end + end + %% Calculate the metric value + function score = CalMetric(obj,metName,Population) + switch metName + case {'Mean_IGD','Mean_HV','Worst_IGD','Worst_HV'} + score = feval(metName,Population,obj); + otherwise + score = feval(metName,Population,obj.optimum); + end + end + %% Perturb solutions multiple times + function PopX = Perturb(obj,PopDec,N) + if nargin < 3; N = obj.H; end + PopX = []; + for i = 1 : N + PopX = [PopX,SOLUTION(PopDec,obj.CalObj(PopDec),obj.CalCon(PopDec))]; + end + end + end +end + +function g = g1(x,t) + g = (3+x-4*t*t*t).^2; +end + +function g = g4(x,t) + g = 2*((x-t).^2)+sin(2*pi*(x-t)).^2; +end \ No newline at end of file diff --git a/PlatEMO/Problems/Multi-objective optimization/Real-world MOPs/Sparse_KP.m b/PlatEMO/Problems/Multi-objective optimization/Real-world MOPs/Sparse_KP.m index 980fdf7cb..ad753fa2e 100644 --- a/PlatEMO/Problems/Multi-objective optimization/Real-world MOPs/Sparse_KP.m +++ b/PlatEMO/Problems/Multi-objective optimization/Real-world MOPs/Sparse_KP.m @@ -1,5 +1,5 @@ classdef Sparse_KP < PROBLEM -% +% % The sparse multi-objective knapsack problem %------------------------------- Reference -------------------------------- diff --git a/PlatEMO/Problems/Single-objective optimization/ISCSO/ISCSO_2016.p b/PlatEMO/Problems/Single-objective optimization/ISCSO/ISCSO_2016.p new file mode 100644 index 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zEmcrZg}Gv+ML#lYsT*UBKPn42t#==DyBy!Hq6YQLXzfeGIR+Sl;L8PG)8me zco?33f`wO0hOa_~tjm@k`YeBY-tA@6|30!dF@D8}_|Nv;>B57>0^ml&?irhUMooFH zg5-%(L#!E@cx67SZkIgM_JO#&+A_Y>gi5?~Q9_y51D)uC2PEpT58Fo7=61WxIwtf? z;92;S6pws52hb=k0*daRIkzNQt$nzs__;;@MGb}XBs^2pQb-YvsO&A0VxF!h5|>5f zuO77w-wNKKm!U3nxKS+stU0k9<>6a{!mV(27&{FiB3K=fY>M4)qD8mr5sa71Xv7?} zH!ur8Cbq1W(dVOtQ4^r~4^$aKFx!D5&g|+b3W-iP-3_v;I=q`S!jo0g=~~9x1CjD?cn!e9y6BO z@SDbuLt>DCKW9B;Wy|49hE4|A6b)dtIW<3AAI!Z63Q7q3OFM85UQdo2T4rM7!L@k3 z`USA~3UjwUQu4GSZ8_~t98FYv3${*rrE1LMF;c0ON#|8CnhR)kGSB>1xOM-o@XITU ze2|5ci^X-dkDTY~LYW`Iq~|WDeBCC{PZ3e2yz13(x=@H%xGaJiuF8cSy6qPcu+mf6 zV|Qv~1yd!_Sd*2-)?$Qe*~VbcHuEx?pc^bbPmUTN)c9vSt+1ZXu6GEy)i7dBO{V7z zxL;n#m-g86W1>>_uFoxju}wOaJcTpqHIo8ZqkN0dQ|7GU{-abTLWt|U;KZ!CpS%x1 z=cd*V!I7}L4AXNVW)#}5p*ijZNWq+>x>6U!+j=UIhYFS4@`a<=`(kp~2=!*_Kro+V zsQ?Lmv8mmEb7MRZM~hb&q$s=!-7(i`Iy;kAiajG)YbZ{OogSL6HW$Cg2YT~FBy}>K zf8@mw +% International student competition in structural optimization + +%------------------------------- Reference -------------------------------- +% S. K. Azad and S. K. Azad, A standard benchmarking suite for structural +% optimization algorithms: ISCSO 2016-2022, Structures, 2023, 58, 105409. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This functions is written by Saeid Kazemzadeh Azad + + methods + %% Default settings of the problem + function Setting(obj) + obj.M = 1; + obj.D = 124; + obj.lower = [ones(1,117),1000,1000,1000,1000,1000,1000,1000]; + obj.upper = [37*ones(1,117),3500,3500,3500,3500,3500,3500,3500]; + obj.encoding = 3*ones(1,obj.D); + end + %% Calculate objective values and constraint violations + function Population = Evaluation(obj,varargin) + X = varargin{1}; + X = max(min(X,repmat(obj.upper,size(X,1),1)),repmat(obj.lower,size(X,1),1)); + PopObj = zeros(size(X,1),1); + PopCon = zeros(size(X,1),2); + for i = 1 : size(X,1) + [PopObj(i,1),PopCon(i,1),PopCon(i,2)] = ISCSO_2016(X(i,1:117),X(i,118:124),0); + end + Population = SOLUTION(X,PopObj,PopCon,varargin{2:end}); + obj.FE = obj.FE + length(Population); + end + end +end \ No newline at end of file diff --git a/PlatEMO/Problems/Single-objective optimization/ISCSO/SO_ISCSO_2017.m b/PlatEMO/Problems/Single-objective optimization/ISCSO/SO_ISCSO_2017.m new file mode 100644 index 000000000..4b58a6067 --- /dev/null +++ b/PlatEMO/Problems/Single-objective optimization/ISCSO/SO_ISCSO_2017.m @@ -0,0 +1,41 @@ +classdef SO_ISCSO_2017 < PROBLEM +% +% International student competition in structural optimization + +%------------------------------- Reference -------------------------------- +% S. K. Azad and S. K. Azad, A standard benchmarking suite for structural +% optimization algorithms: ISCSO 2016-2022, Structures, 2023, 58, 105409. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This functions is written by Saeid Kazemzadeh Azad + + methods + %% Default settings of the problem + function Setting(obj) + obj.M = 1; + obj.D = 211; + obj.lower = [ones(1,198),25000,25000,25000,25000,25000,25000,-12000,-12000,-12000,-12000,-12000,-12000,-12000]; + obj.upper = [37*ones(1,198),35000,35000,35000,35000,35000,35000,3000,3000,3000,3000,3000,3000,3000]; + obj.encoding = 3*ones(1,obj.D); + end + %% Calculate objective values and constraint violations + function Population = Evaluation(obj,varargin) + X = varargin{1}; + X = max(min(X,repmat(obj.upper,size(X,1),1)),repmat(obj.lower,size(X,1),1)); + PopObj = zeros(size(X,1),1); + PopCon = zeros(size(X,1),2); + for i = 1 : size(X,1) + [PopObj(i,1),PopCon(i,1),PopCon(i,2)] = ISCSO_2017(X(i,1:198),X(i,199:211),0); + end + Population = SOLUTION(X,PopObj,PopCon,varargin{2:end}); + obj.FE = obj.FE + length(Population); + end + end +end \ No newline at end of file diff --git a/PlatEMO/Problems/Single-objective optimization/ISCSO/SO_ISCSO_2018.m b/PlatEMO/Problems/Single-objective optimization/ISCSO/SO_ISCSO_2018.m new file mode 100644 index 000000000..b6cb115bb --- /dev/null +++ b/PlatEMO/Problems/Single-objective optimization/ISCSO/SO_ISCSO_2018.m @@ -0,0 +1,41 @@ +classdef SO_ISCSO_2018 < PROBLEM +% +% International student competition in structural optimization + +%------------------------------- Reference -------------------------------- +% S. K. Azad and S. K. Azad, A standard benchmarking suite for structural +% optimization algorithms: ISCSO 2016-2022, Structures, 2023, 58, 105409. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This functions is written by Saeid Kazemzadeh Azad + + methods + %% Default settings of the problem + function Setting(obj) + obj.M = 1; + obj.D = 328; + obj.lower = [ones(1,314),9000,9000,9000,9000,9000,9000,9000,9000,9000,9000,9000,9000,9000,9000]; + obj.upper = [37*ones(1,314),20000,20000,20000,20000,20000,20000,20000,20000,20000,20000,20000,20000,20000,20000]; + obj.encoding = 3*ones(1,obj.D); + end + %% Calculate objective values and constraint violations + function Population = Evaluation(obj,varargin) + X = varargin{1}; + X = max(min(X,repmat(obj.upper,size(X,1),1)),repmat(obj.lower,size(X,1),1)); + PopObj = zeros(size(X,1),1); + PopCon = zeros(size(X,1),2); + for i = 1 : size(X,1) + [PopObj(i,1),PopCon(i,1),PopCon(i,2)] = ISCSO_2018(X(i,1:314),X(i,315:328),0); + end + Population = SOLUTION(X,PopObj,PopCon,varargin{2:end}); + obj.FE = obj.FE + length(Population); + end + end +end \ No newline at end of file diff --git a/PlatEMO/Problems/Single-objective optimization/ISCSO/SO_ISCSO_2019.m b/PlatEMO/Problems/Single-objective optimization/ISCSO/SO_ISCSO_2019.m new file mode 100644 index 000000000..f216617d9 --- /dev/null +++ b/PlatEMO/Problems/Single-objective optimization/ISCSO/SO_ISCSO_2019.m @@ -0,0 +1,41 @@ +classdef SO_ISCSO_2019 < PROBLEM +% +% International student competition in structural optimization + +%------------------------------- Reference -------------------------------- +% S. K. Azad and S. K. Azad, A standard benchmarking suite for structural +% optimization algorithms: ISCSO 2016-2022, Structures, 2023, 58, 105409. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This functions is written by Saeid Kazemzadeh Azad + + methods + %% Default settings of the problem + function Setting(obj) + obj.M = 1; + obj.D = 270; + obj.lower = [ones(1,260),-25000,-25000,-25000,-25000,-25000,-25000,-25000,-25000,-25000,-25000]; + obj.upper = [37*ones(1,260),3500,3500,3500,3500,3500,3500,3500,3500,3500,3500]; + obj.encoding = 3*ones(1,obj.D); + end + %% Calculate objective values and constraint violations + function Population = Evaluation(obj,varargin) + X = varargin{1}; + X = max(min(X,repmat(obj.upper,size(X,1),1)),repmat(obj.lower,size(X,1),1)); + PopObj = zeros(size(X,1),1); + PopCon = zeros(size(X,1),2); + for i = 1 : size(X,1) + [PopObj(i,1),PopCon(i,1),PopCon(i,2)] = ISCSO_2019(X(i,1:260),X(i,261:270),0); + end + Population = SOLUTION(X,PopObj,PopCon,varargin{2:end}); + obj.FE = obj.FE + length(Population); + end + end +end \ No newline at end of file diff --git a/PlatEMO/Problems/Single-objective optimization/ISCSO/SO_ISCSO_2021.m b/PlatEMO/Problems/Single-objective optimization/ISCSO/SO_ISCSO_2021.m new file mode 100644 index 000000000..4b2f016f4 --- /dev/null +++ b/PlatEMO/Problems/Single-objective optimization/ISCSO/SO_ISCSO_2021.m @@ -0,0 +1,41 @@ +classdef SO_ISCSO_2021 < PROBLEM +% +% International student competition in structural optimization + +%------------------------------- Reference -------------------------------- +% S. K. Azad and S. K. Azad, A standard benchmarking suite for structural +% optimization algorithms: ISCSO 2016-2022, Structures, 2023, 58, 105409. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This functions is written by Saeid Kazemzadeh Azad + + methods + %% Default settings of the problem + function Setting(obj) + obj.M = 1; + obj.D = 345; + obj.lower = ones(1,obj.D); + obj.upper = 37*ones(1,obj.D); + obj.encoding = 3*ones(1,obj.D); + end + %% Calculate objective values and constraint violations + function Population = Evaluation(obj,varargin) + X = varargin{1}; + X = max(min(X,repmat(obj.upper,size(X,1),1)),repmat(obj.lower,size(X,1),1)); + PopObj = zeros(size(X,1),1); + PopCon = zeros(size(X,1),2); + for i = 1 : size(X,1) + [PopObj(i,1),PopCon(i,1),PopCon(i,2)] = ISCSO_2021(X(i,:),0); + end + Population = SOLUTION(X,PopObj,PopCon,varargin{2:end}); + obj.FE = obj.FE + length(Population); + end + end +end \ No newline at end of file diff --git a/PlatEMO/Problems/Single-objective optimization/ISCSO/SO_ISCSO_2022.m b/PlatEMO/Problems/Single-objective optimization/ISCSO/SO_ISCSO_2022.m new file mode 100644 index 000000000..682294c15 --- /dev/null +++ b/PlatEMO/Problems/Single-objective optimization/ISCSO/SO_ISCSO_2022.m @@ -0,0 +1,40 @@ +classdef SO_ISCSO_2022 < PROBLEM +% +% International student competition in structural optimization + +%------------------------------- Reference -------------------------------- +% S. K. Azad and S. K. Azad, A standard benchmarking suite for structural +% optimization algorithms: ISCSO 2016-2022, Structures, 2023, 58, 105409. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This functions is written by Saeid Kazemzadeh Azad + methods + %% Default settings of the problem + function Setting(obj) + obj.M = 1; + obj.D = 336; + obj.lower = ones(1,obj.D); + obj.upper = 37*ones(1,obj.D); + obj.encoding = 3*ones(1,obj.D); + end + %% Calculate objective values and constraint violations + function Population = Evaluation(obj,varargin) + X = varargin{1}; + X = max(min(X,repmat(obj.upper,size(X,1),1)),repmat(obj.lower,size(X,1),1)); + PopObj = zeros(size(X,1),1); + PopCon = zeros(size(X,1),2); + for i = 1 : size(X,1) + [PopObj(i,1),PopCon(i,1),PopCon(i,2)] = ISCSO_2022(X(i,:),0); + end + Population = SOLUTION(X,PopObj,PopCon,varargin{2:end}); + obj.FE = obj.FE + length(Population); + end + end +end \ No newline at end of file diff --git a/PlatEMO/Problems/UserProblem.m b/PlatEMO/Problems/UserProblem.m index 4b45ae152..6e027ed83 100644 --- a/PlatEMO/Problems/UserProblem.m +++ b/PlatEMO/Problems/UserProblem.m @@ -15,6 +15,7 @@ % conFcn constraint functions % gradFcn function for calculating the gradients of objectives and constraints % data data of the problem +% once whether the inputs of evalFcn, decFcn, objFcn, conFcn can be multiple solutions %------------------------------- Copyright -------------------------------- % Copyright (c) 2024 BIMK Group. You are free to use the PlatEMO for @@ -26,19 +27,20 @@ %-------------------------------------------------------------------------- properties(SetAccess = protected) - initFcn = {}; % Function for initializing solutions - evalFcn = {}; % Function for evaluating solutions - decFcn = {}; % Function for repairing invalid solutions - objFcn = {}; % Objective functions - conFcn = {}; % Constraint functions - gradFcn = {}; % Function for calculating the gradients of objectives and constraints - data = {}; % Data of the problem + initFcn = {}; % Function for initializing solutions + evalFcn = {}; % Function for evaluating solutions + decFcn = {}; % Function for repairing invalid solutions + objFcn = {}; % Objective functions + conFcn = {}; % Constraint functions + gradFcn = {}; % Function for calculating the gradients of objectives and constraints + data = {}; % Data of the problem + once = false; % Whether the inputs of evalFcn, decFcn, objFcn, conFcn can be multiple solutions end methods %% Constructor function obj = UserProblem(varargin) isStr = find(cellfun(@ischar,varargin(1:end-1))&~cellfun(@isempty,varargin(2:end))); - for i = isStr(ismember(varargin(isStr),{'N','M','D','maxFE','maxRuntime','encoding','lower','upper','initFcn','evalFcn','decFcn','objFcn','conFcn','gradFcn','data'})) + for i = isStr(ismember(varargin(isStr),{'N','M','D','maxFE','maxRuntime','encoding','lower','upper','initFcn','evalFcn','decFcn','objFcn','conFcn','gradFcn','data','once'})) obj.(varargin{i}) = varargin{i+1}; end if isempty(obj.D) @@ -74,8 +76,12 @@ %% Evaluate multiple solutions function Population = Evaluation(obj,varargin) if ~isempty(obj.evalFcn) - for i = 1 : size(varargin{1},1) - [PopDec(i,:),PopObj(i,:),PopCon(i,:)] = CallFcn(obj.evalFcn,varargin{1}(i,:),obj.data,'evaluation function',[1 obj.D]); + if obj.once + [PopDec,PopObj,PopCon] = CallFcn(obj.evalFcn,varargin{1},obj.data,'evaluation function',[size(varargin{1},1) obj.D],[size(varargin{1},1) 1],[size(varargin{1},1) 1]); + else + for i = 1 : size(varargin{1},1) + [PopDec(i,:),PopObj(i,:),PopCon(i,:)] = CallFcn(obj.evalFcn,varargin{1}(i,:),obj.data,'evaluation function',[1 obj.D],[1 1],[1 1]); + end end Population = SOLUTION(PopDec,PopObj,PopCon,varargin{2:end}); obj.FE = obj.FE + length(Population); @@ -86,8 +92,12 @@ %% Repair invalid solutions function PopDec = CalDec(obj,PopDec) if ~isempty(obj.decFcn) - for i = 1 : size(PopDec,1) - PopDec(i,:) = CallFcn(obj.decFcn,PopDec(i,:),obj.data,'repair function',[1 obj.D]); + if obj.once + PopDec = CallFcn(obj.decFcn,PopDec,obj.data,'repair function',[size(PopDec,1) obj.D]); + else + for i = 1 : size(PopDec,1) + PopDec(i,:) = CallFcn(obj.decFcn,PopDec(i,:),obj.data,'repair function',[1 obj.D]); + end end else PopDec = CalDec@PROBLEM(obj,PopDec); @@ -97,9 +107,13 @@ function PopObj = CalObj(obj,PopDec) if ~isempty(obj.objFcn) PopObj = zeros(size(PopDec,1),length(obj.objFcn)); - for i = 1 : size(PopDec,1) - for j = 1 : length(obj.objFcn) - PopObj(i,j) = CallFcn(obj.objFcn{j},PopDec(i,:),obj.data,sprintf('objective function f%d',j),[1 1]); + for j = 1 : length(obj.objFcn) + if obj.once + PopObj(:,j) = CallFcn(obj.objFcn{j},PopDec,obj.data,sprintf('objective function f%d',j),[size(PopDec,1) 1]); + else + for i = 1 : size(PopDec,1) + PopObj(i,j) = CallFcn(obj.objFcn{j},PopDec(i,:),obj.data,sprintf('objective function f%d',j),[1 1]); + end end end else @@ -110,9 +124,13 @@ function PopCon = CalCon(obj,PopDec) if ~isempty(obj.conFcn) PopCon = zeros(size(PopDec,1),length(obj.conFcn)); - for i = 1 : size(PopDec,1) - for j = 1 : length(obj.conFcn) - PopCon(i,j) = CallFcn(obj.conFcn{j},PopDec(i,:),obj.data,sprintf('constraint function g%d',j),[1 1]); + for j = 1 : length(obj.conFcn) + if obj.once + PopCon(:,j) = CallFcn(obj.conFcn{j},PopDec,obj.data,sprintf('constraint function g%d',j),[size(PopDec,1) 1]); + else + for i = 1 : size(PopDec,1) + PopCon(i,j) = CallFcn(obj.conFcn{j},PopDec(i,:),obj.data,sprintf('constraint function g%d',j),[1 1]); + end end end else diff --git a/PlatEMO/manual (Chinese).pdf b/PlatEMO/manual (Chinese).pdf index 155ff43c7f72c75e65d57bef5bd63f7bba7b6a32..a5694ca7d4248b697bc9b76f8e359483f5034a17 100644 GIT binary patch delta 1069250 zcmb5VWk8kR@&@XrrMpuQq}eoxba!{BAPv&6kq!yjz@}5Wq#Nn(7Ni>lqy&_>8}OX- zJLiAzhx=jk)~vN=o|!eX-sfF>IX6%LU=u5g!^1_%MyblGVBuuhhyvO$>!S^J=#{-_& z!v}`}2*HH_ZX6)+NeBphk^%y`!Pfv=Qc8Aip2ybg+lqk^f1@S9G$4h1@&I~?vxHy5W*5#(S*O`{M13qiNWK0xm9*O;XY)r(z+M` z;A}qjg*5Z$LfU7up96J&T7ujQ(pdx%UH-`Ran=7Rx$<~F;jYDTwq5eA^|(0-{j%uz z=pHXKdXL4jDJ$WPcMPe_?@XOHa0YYl`+J_t(1y}hgyR0WaoKGltoAL zH7B1{mMM?w7#R_Jj*)LDGy1TV%DP^Nk#tv*LlUgDvqa?aTTQ~a$>WNSF=z|80jVoe zH+7)g`2t=>-Fz6l~H+NMYGOzKI{ z;Y^!ME*mx_wd@&W`cO0*sbt>Bs-Mg&qQg?nChxR>t5UvPwjN2T9utGZ8~8$QlIEzm z|Am7XW8(6?!kULHlQIWtnYLjJDJQtpq)0)qzLM9ehG>Vu$HXg$zxBhnk;L38m2Rv}JNEYGo2zWE zI~f`wb;ACy+nkQ4;gFJ-DslVQ7XBtT(v1loN$<&`jtX|xxpgl2_m6(tgrFaQeFyJq zgvsAo$qq%RL^)u+@HVGTB!j_Z-oV_J}a zJ*V0gi#eKglonyw~ 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