diff --git a/docs/conf.py b/docs/conf.py
index 56e0eceb..a9ad411d 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -20,50 +20,50 @@
 # directory, add these directories to sys.path here. If the directory is
 # relative to the documentation root, use os.path.abspath to make it
 # absolute, like shown here.
-#sys.path.insert(0, os.path.abspath('.'))
+# sys.path.insert(0, os.path.abspath('.'))
 
 # Get the project root dir, which is the parent dir of this
-#cwd = os.getcwd()
-#project_root = os.path.dirname(cwd)
+# cwd = os.getcwd()
+# project_root = os.path.dirname(cwd)
 
 # Insert the project root dir as the first element in the PYTHONPATH.
 # This lets us ensure that the source package is imported, and that its
 # version is used.
-#sys.path.insert(0, project_root)
-sys.path.insert(0, os.path.abspath('../'))
+# sys.path.insert(0, project_root)
+sys.path.insert(0, os.path.abspath("../"))
 
 import pyswarms
 
 # -- General configuration ---------------------------------------------
 
 # If your documentation needs a minimal Sphinx version, state it here.
-#needs_sphinx = '1.0'
+# needs_sphinx = '1.0'
 
 # Add any Sphinx extension module names here, as strings. They can be
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
 extensions = [
-    'sphinx.ext.autodoc',
-    'sphinx.ext.viewcode',
-    'sphinx.ext.napoleon',
-    'sphinx.ext.mathjax'
-    ]
+    "sphinx.ext.autodoc",
+    "sphinx.ext.viewcode",
+    "sphinx.ext.napoleon",
+    "sphinx.ext.mathjax",
+]
 
-exclude_patterns = ['_build', '**.ipynb_checkpoints']
+exclude_patterns = ["_build", "**.ipynb_checkpoints"]
 
 # Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
+templates_path = ["_templates"]
 
 # The suffix of source filenames.
-source_suffix = '.rst'
+source_suffix = ".rst"
 
 # The encoding of source files.
-#source_encoding = 'utf-8-sig'
+# source_encoding = 'utf-8-sig'
 
 # The master toctree document.
-master_doc = 'index'
+master_doc = "index"
 
 # General information about the project.
-project = u'PySwarms'
+project = u"PySwarms"
 copyright = u"2017, Lester James V. Miranda"
 
 # The version info for the project you're documenting, acts as replacement
@@ -77,174 +77,177 @@
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
-#language = None
+# language = None
 
 # There are two options for replacing |today|: either, you set today to
 # some non-false value, then it is used:
-#today = ''
+# today = ''
 # Else, today_fmt is used as the format for a strftime call.
-#today_fmt = '%B %d, %Y'
+# today_fmt = '%B %d, %Y'
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
-exclude_patterns = ['_build']
+exclude_patterns = ["_build"]
 
 # The reST default role (used for this markup: `text`) to use for all
 # documents.
-#default_role = None
+# default_role = None
 
 # If true, '()' will be appended to :func: etc. cross-reference text.
-#add_function_parentheses = True
+# add_function_parentheses = True
 
 # If true, the current module name will be prepended to all description
 # unit titles (such as .. function::).
-#add_module_names = True
+# add_module_names = True
 
 # If true, sectionauthor and moduleauthor directives will be shown in the
 # output. They are ignored by default.
-#show_authors = False
+# show_authors = False
 
 # The name of the Pygments (syntax highlighting) style to use.
-pygments_style = 'sphinx'
+pygments_style = "sphinx"
 
 # A list of ignored prefixes for module index sorting.
-#modindex_common_prefix = []
+# modindex_common_prefix = []
 
 # If true, keep warnings as "system message" paragraphs in the built
 # documents.
-#keep_warnings = False
+# keep_warnings = False
 
 
 # -- Options for HTML output -------------------------------------------
 
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
-html_theme = 'sphinx_rtd_theme'
+html_theme = "sphinx_rtd_theme"
 
 # Theme options are theme-specific and customize the look and feel of a
 # theme further.  For a list of options available for each theme, see the
 # documentation.
-#html_theme_options = {}
+# html_theme_options = {}
 
 # Add any paths that contain custom themes here, relative to this directory.
-#html_theme_path = []
+# html_theme_path = []
 
 # The name for this set of Sphinx documents.  If None, it defaults to
 # "<project> v<release> documentation".
-#html_title = None
+# html_title = None
 
 # A shorter title for the navigation bar.  Default is the same as
 # html_title.
-#html_short_title = None
+# html_short_title = None
 
 # The name of an image file (relative to this directory) to place at the
 # top of the sidebar.
-#html_logo = None
+# html_logo = None
 
 # The name of an image file (within the static path) to use as favicon
 # of the docs.  This file should be a Windows icon file (.ico) being
 # 16x16 or 32x32 pixels large.
-#html_favicon = None
+# html_favicon = None
 
 # Add any paths that contain custom static files (such as style sheets)
 # here, relative to this directory. They are copied after the builtin
 # static files, so a file named "default.css" will overwrite the builtin
 # "default.css".
-html_static_path = ['_static']
+html_static_path = ["_static"]
+
 
 def setup(app):
     # overrides for wide tables in RTD theme
-    app.add_stylesheet('theme_overrides.css') # path relative to static
+    app.add_stylesheet("theme_overrides.css")  # path relative to static
 
 
 # If not '', a 'Last updated on:' timestamp is inserted at every page
 # bottom, using the given strftime format.
-html_last_updated_fmt = '%b %d, %Y'
+html_last_updated_fmt = "%b %d, %Y"
 
 # If true, SmartyPants will be used to convert quotes and dashes to
 # typographically correct entities.
-#html_use_smartypants = True
+# html_use_smartypants = True
 
 # Custom sidebar templates, maps document names to template names.
-#html_sidebars = {}
+# html_sidebars = {}
 
 # Additional templates that should be rendered to pages, maps page names
 # to template names.
-#html_additional_pages = {}
+# html_additional_pages = {}
 
 # If false, no module index is generated.
-#html_domain_indices = True
+# html_domain_indices = True
 
 # If false, no index is generated.
-#html_use_index = True
+# html_use_index = True
 
 # If true, the index is split into individual pages for each letter.
-#html_split_index = False
+# html_split_index = False
 
 # If true, links to the reST sources are added to the pages.
-#html_show_sourcelink = True
+# html_show_sourcelink = True
 
 # If true, "Created using Sphinx" is shown in the HTML footer.
 # Default is True.
-#html_show_sphinx = True
+# html_show_sphinx = True
 
 # If true, "(C) Copyright ..." is shown in the HTML footer.
 # Default is True.
-#html_show_copyright = True
+# html_show_copyright = True
 
 # If true, an OpenSearch description file will be output, and all pages
 # will contain a <link> tag referring to it.  The value of this option
 # must be the base URL from which the finished HTML is served.
-#html_use_opensearch = ''
+# html_use_opensearch = ''
 
 # This is the file name suffix for HTML files (e.g. ".xhtml").
-#html_file_suffix = None
+# html_file_suffix = None
 
 # Output file base name for HTML help builder.
-htmlhelp_basename = 'pyswarmsdoc'
+htmlhelp_basename = "pyswarmsdoc"
 
 
 # -- Options for LaTeX output ------------------------------------------
 
 latex_elements = {
     # The paper size ('letterpaper' or 'a4paper').
-    'papersize': 'a4paper',
-
+    "papersize": "a4paper",
     # The font size ('10pt', '11pt' or '12pt').
-    #'pointsize': '10pt',
-
+    # 'pointsize': '10pt',
     # Additional stuff for the LaTeX preamble.
-    #'preamble': '',
+    # 'preamble': '',
 }
 
 # Grouping the document tree into LaTeX files. List of tuples
 # (source start file, target name, title, author, documentclass
 # [howto/manual]).
 latex_documents = [
-    ('index', 'pyswarms.tex',
-     u'PySwarms Documentation',
-     u'Lester James V. Miranda', 'manual'),
+    (
+        "index",
+        "pyswarms.tex",
+        u"PySwarms Documentation",
+        u"Lester James V. Miranda",
+        "manual",
+    )
 ]
 
 # The name of an image file (relative to this directory) to place at
 # the top of the title page.
-#latex_logo = None
+# latex_logo = None
 
 # For "manual" documents, if this is true, then toplevel headings
 # are parts, not chapters.
-#latex_use_parts = False
+# latex_use_parts = False
 
 # If true, show page references after internal links.
-#latex_show_pagerefs = False
+# latex_show_pagerefs = False
 
 # If true, show URL addresses after external links.
-#latex_show_urls = False
+# latex_show_urls = False
 
 # Documents to append as an appendix to all manuals.
-#latex_appendices = []
+# latex_appendices = []
 
 # If false, no module index is generated.
-#latex_domain_indices = True
+# latex_domain_indices = True
 
 
 # -- Options for manual page output ------------------------------------
@@ -252,13 +255,17 @@ def setup(app):
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
 man_pages = [
-    ('index', 'pyswarms',
-     u'PySwarms Documentation',
-     [u'Lester James V. Miranda'], 1)
+    (
+        "index",
+        "pyswarms",
+        u"PySwarms Documentation",
+        [u"Lester James V. Miranda"],
+        1,
+    )
 ]
 
 # If true, show URL addresses after external links.
-#man_show_urls = False
+# man_show_urls = False
 
 
 # -- Options for Texinfo output ----------------------------------------
@@ -267,22 +274,25 @@ def setup(app):
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    ('index', 'pyswarms',
-     u'PySwarms Documentation',
-     u'Lester James V. Miranda',
-     'pyswarms',
-     'PySwarms is a simple, Python-based, Particle Swarm Optimization (PSO) library.',
-     'Research'),
+    (
+        "index",
+        "pyswarms",
+        u"PySwarms Documentation",
+        u"Lester James V. Miranda",
+        "pyswarms",
+        "PySwarms is a simple, Python-based, Particle Swarm Optimization (PSO) library.",
+        "Research",
+    )
 ]
 
 # Documents to append as an appendix to all manuals.
-#texinfo_appendices = []
+# texinfo_appendices = []
 
 # If false, no module index is generated.
-#texinfo_domain_indices = True
+# texinfo_domain_indices = True
 
 # How to display URL addresses: 'footnote', 'no', or 'inline'.
-#texinfo_show_urls = 'footnote'
+# texinfo_show_urls = 'footnote'
 
 # If true, do not generate a @detailmenu in the "Top" node's menu.
-#texinfo_no_detailmenu = False
+# texinfo_no_detailmenu = False
diff --git a/pyswarms/backend/topology/pyramid.py b/pyswarms/backend/topology/pyramid.py
index 1f209502..f1af7713 100644
--- a/pyswarms/backend/topology/pyramid.py
+++ b/pyswarms/backend/topology/pyramid.py
@@ -61,24 +61,39 @@ def compute_gbest(self, swarm):
         try:
             # If there are less than (swarm.dimensions + 1) particles they are all connected
             if swarm.n_particles < swarm.dimensions + 1:
-                self.neighbor_idx = np.tile(np.arange(swarm.n_particles), (swarm.n_particles, 1))
+                self.neighbor_idx = np.tile(
+                    np.arange(swarm.n_particles), (swarm.n_particles, 1)
+                )
                 best_pos = swarm.pbest_pos[np.argmin(swarm.pbest_cost)]
                 best_cost = np.min(swarm.pbest_cost)
             else:
                 # Check if the topology is static or dynamic and assign neighbors
-                if (self.static and self.neighbor_idx is None) or not self.static:
-                    pyramid = Delaunay(swarm.position, qhull_options="QJ0.001 Qbb Qc Qx")
+                if (
+                    self.static and self.neighbor_idx is None
+                ) or not self.static:
+                    pyramid = Delaunay(
+                        swarm.position, qhull_options="QJ0.001 Qbb Qc Qx"
+                    )
                     indices, index_pointer = pyramid.vertex_neighbor_vertices
                     # Insert all the neighbors for each particle in the idx array
                     self.neighbor_idx = np.array(
-                        [index_pointer[indices[i]:indices[i + 1]] for i in range(swarm.n_particles)]
+                        [
+                            index_pointer[indices[i] : indices[i + 1]]
+                            for i in range(swarm.n_particles)
+                        ]
                     )
 
                 idx_min = np.array(
-                    [swarm.pbest_cost[self.neighbor_idx[i]].argmin() for i in range(len(self.neighbor_idx))]
+                    [
+                        swarm.pbest_cost[self.neighbor_idx[i]].argmin()
+                        for i in range(len(self.neighbor_idx))
+                    ]
                 )
                 best_neighbor = np.array(
-                    [self.neighbor_idx[i][idx_min[i]] for i in range(len(self.neighbor_idx))]
+                    [
+                        self.neighbor_idx[i][idx_min[i]]
+                        for i in range(len(self.neighbor_idx))
+                    ]
                 ).astype(int)
 
                 # Obtain best cost and position
diff --git a/pyswarms/backend/topology/random.py b/pyswarms/backend/topology/random.py
index 17439621..91464ee6 100644
--- a/pyswarms/backend/topology/random.py
+++ b/pyswarms/backend/topology/random.py
@@ -15,7 +15,7 @@
 from scipy.sparse.csgraph import connected_components, dijkstra
 
 # Import from package
-from ..import operators as ops
+from .. import operators as ops
 from .base import Topology
 
 # Create a logger
@@ -65,10 +65,23 @@ def compute_gbest(self, swarm, k):
             # Check if the topology is static or dynamic and assign neighbors
             if (self.static and self.neighbor_idx is None) or not self.static:
                 adj_matrix = self.__compute_neighbors(swarm, k)
-                self.neighbor_idx = np.array([adj_matrix[i].nonzero()[0] for i in range(swarm.n_particles)])
-            idx_min = np.array([swarm.pbest_cost[self.neighbor_idx[i]].argmin() for i in range(len(self.neighbor_idx))])
+                self.neighbor_idx = np.array(
+                    [
+                        adj_matrix[i].nonzero()[0]
+                        for i in range(swarm.n_particles)
+                    ]
+                )
+            idx_min = np.array(
+                [
+                    swarm.pbest_cost[self.neighbor_idx[i]].argmin()
+                    for i in range(len(self.neighbor_idx))
+                ]
+            )
             best_neighbor = np.array(
-                [self.neighbor_idx[i][idx_min[i]] for i in range(len(self.neighbor_idx))]
+                [
+                    self.neighbor_idx[i][idx_min[i]]
+                    for i in range(len(self.neighbor_idx))
+                ]
             ).astype(int)
 
             # Obtain best cost and position
@@ -186,23 +199,43 @@ def __compute_neighbors(self, swarm, k):
         adj_matrix = np.identity(swarm.n_particles, dtype=int)
 
         neighbor_matrix = np.array(
-            [np.random.choice(
-                # Exclude i from the array
-                np.setdiff1d(
-                    np.arange(swarm.n_particles), np.array([i])
-                ), k, replace=False
-            ) for i in range(swarm.n_particles)])
+            [
+                np.random.choice(
+                    # Exclude i from the array
+                    np.setdiff1d(np.arange(swarm.n_particles), np.array([i])),
+                    k,
+                    replace=False,
+                )
+                for i in range(swarm.n_particles)
+            ]
+        )
 
         # Set random elements to one using the neighbor matrix
-        adj_matrix[np.arange(swarm.n_particles).reshape(swarm.n_particles, 1), neighbor_matrix] = 1
-        adj_matrix[neighbor_matrix, np.arange(swarm.n_particles).reshape(swarm.n_particles, 1)] = 1
-
-        dist_matrix = dijkstra(adj_matrix, directed=False, return_predecessors=False, unweighted=True)
+        adj_matrix[
+            np.arange(swarm.n_particles).reshape(swarm.n_particles, 1),
+            neighbor_matrix,
+        ] = 1
+        adj_matrix[
+            neighbor_matrix,
+            np.arange(swarm.n_particles).reshape(swarm.n_particles, 1),
+        ] = 1
+
+        dist_matrix = dijkstra(
+            adj_matrix,
+            directed=False,
+            return_predecessors=False,
+            unweighted=True,
+        )
 
         # Generate connected graph.
-        while connected_components(adj_matrix, directed=False, return_labels=False) != 1:
+        while (
+            connected_components(
+                adj_matrix, directed=False, return_labels=False
+            )
+            != 1
+        ):
             for i, j in itertools.product(range(swarm.n_particles), repeat=2):
-                    if dist_matrix[i][j] == np.inf:
-                        adj_matrix[i][j] = 1
+                if dist_matrix[i][j] == np.inf:
+                    adj_matrix[i][j] = 1
 
         return adj_matrix
diff --git a/pyswarms/backend/topology/ring.py b/pyswarms/backend/topology/ring.py
index bef98219..660899c4 100644
--- a/pyswarms/backend/topology/ring.py
+++ b/pyswarms/backend/topology/ring.py
@@ -72,12 +72,14 @@ def compute_gbest(self, swarm, p, k):
             # independently of each other.
             if k == 1:
                 # The minimum index is itself, no mapping needed.
-                best_neighbor = swarm.pbest_cost[self.neighbor_idx][:, np.newaxis].argmin(
-                    axis=0
-                )
+                best_neighbor = swarm.pbest_cost[self.neighbor_idx][
+                    :, np.newaxis
+                ].argmin(axis=0)
             else:
                 idx_min = swarm.pbest_cost[self.neighbor_idx].argmin(axis=1)
-                best_neighbor = self.neighbor_idx[np.arange(len(self.neighbor_idx)), idx_min]
+                best_neighbor = self.neighbor_idx[
+                    np.arange(len(self.neighbor_idx)), idx_min
+                ]
             # Obtain best cost and position
             best_cost = np.min(swarm.pbest_cost[best_neighbor])
             best_pos = swarm.pbest_pos[
diff --git a/pyswarms/backend/topology/star.py b/pyswarms/backend/topology/star.py
index 8c666b2d..8a8f3750 100644
--- a/pyswarms/backend/topology/star.py
+++ b/pyswarms/backend/topology/star.py
@@ -62,7 +62,9 @@ def compute_gbest(self, swarm):
         """
         try:
             if self.neighbor_idx is None:
-                self.neighbor_idx = np.tile(np.arange(swarm.n_particles), (swarm.n_particles, 1))
+                self.neighbor_idx = np.tile(
+                    np.arange(swarm.n_particles), (swarm.n_particles, 1)
+                )
             best_pos = swarm.pbest_pos[np.argmin(swarm.pbest_cost)]
             best_cost = np.min(swarm.pbest_cost)
         except AttributeError:
diff --git a/pyswarms/backend/topology/von_neumann.py b/pyswarms/backend/topology/von_neumann.py
index ad44cb85..168ec8ec 100644
--- a/pyswarms/backend/topology/von_neumann.py
+++ b/pyswarms/backend/topology/von_neumann.py
@@ -71,8 +71,9 @@ def delannoy(d, r):
         if d == 0 or r == 0:
             return 1
         else:
-            del_number = (VonNeumann.delannoy(d - 1, r)
-                          + VonNeumann.delannoy(d - 1, r - 1)
-                          + VonNeumann.delannoy(d, r - 1)
-                          )
+            del_number = (
+                VonNeumann.delannoy(d - 1, r)
+                + VonNeumann.delannoy(d - 1, r - 1)
+                + VonNeumann.delannoy(d, r - 1)
+            )
             return del_number
diff --git a/pyswarms/discrete/binary.py b/pyswarms/discrete/binary.py
index 72d8d536..93c481e6 100644
--- a/pyswarms/discrete/binary.py
+++ b/pyswarms/discrete/binary.py
@@ -149,7 +149,9 @@ def __init__(
         # Initialize the topology
         self.top = Ring(static=False)
 
-    def optimize(self, objective_func, iters, print_step=1, verbose=1, **kwargs):
+    def optimize(
+        self, objective_func, iters, print_step=1, verbose=1, **kwargs
+    ):
         """Optimize the swarm for a number of iterations
 
         Performs the optimization to evaluate the objective
@@ -174,13 +176,21 @@ def optimize(self, objective_func, iters, print_step=1, verbose=1, **kwargs):
             the local best cost and the local best position among the
             swarm.
         """
-        cli_print("Arguments Passed to Objective Function: {}".format(kwargs),
-                  verbose, 2, logger=self.logger)
+        cli_print(
+            "Arguments Passed to Objective Function: {}".format(kwargs),
+            verbose,
+            2,
+            logger=self.logger,
+        )
 
         for i in range(iters):
             # Compute cost for current position and personal best
-            self.swarm.current_cost = objective_func(self.swarm.position, **kwargs)
-            self.swarm.pbest_cost = objective_func(self.swarm.pbest_pos, **kwargs)
+            self.swarm.current_cost = objective_func(
+                self.swarm.position, **kwargs
+            )
+            self.swarm.pbest_cost = objective_func(
+                self.swarm.pbest_pos, **kwargs
+            )
             self.swarm.pbest_pos, self.swarm.pbest_cost = compute_pbest(
                 self.swarm
             )
@@ -192,7 +202,9 @@ def optimize(self, objective_func, iters, print_step=1, verbose=1, **kwargs):
             # Print to console
             if i % print_step == 0:
                 cli_print(
-                    "Iteration {}/{}, cost: {}".format(i + 1, iters, np.min(self.swarm.best_cost)),
+                    "Iteration {}/{}, cost: {}".format(
+                        i + 1, iters, np.min(self.swarm.best_cost)
+                    ),
                     verbose,
                     2,
                     logger=self.logger,
diff --git a/pyswarms/single/general_optimizer.py b/pyswarms/single/general_optimizer.py
index d127e582..27edc268 100644
--- a/pyswarms/single/general_optimizer.py
+++ b/pyswarms/single/general_optimizer.py
@@ -206,8 +206,8 @@ def __init__(
                 if (
                     self.r <= 0
                     or not 0
-                           <= VonNeumann.delannoy(self.swarm.dimensions, self.r)
-                           <= self.n_particles - 1
+                    <= VonNeumann.delannoy(self.swarm.dimensions, self.r)
+                    <= self.n_particles - 1
                 ):
                     raise ValueError(
                         "The range must be set such that the computed"
@@ -215,7 +215,9 @@ def __init__(
                         "between 0 and the no. of particles."
                     )
 
-    def optimize(self, objective_func, iters, print_step=1, verbose=1, **kwargs):
+    def optimize(
+        self, objective_func, iters, print_step=1, verbose=1, **kwargs
+    ):
         """Optimize the swarm for a number of iterations
 
         Performs the optimization to evaluate the objective
@@ -240,19 +242,29 @@ def optimize(self, objective_func, iters, print_step=1, verbose=1, **kwargs):
             the global best cost and the global best position.
         """
 
-        cli_print("Arguments Passed to Objective Function: {}".format(kwargs),
-                  verbose, 2, logger=self.logger)
+        cli_print(
+            "Arguments Passed to Objective Function: {}".format(kwargs),
+            verbose,
+            2,
+            logger=self.logger,
+        )
 
         for i in range(iters):
             # Compute cost for current position and personal best
-            self.swarm.current_cost = objective_func(self.swarm.position, **kwargs)
-            self.swarm.pbest_cost = objective_func(self.swarm.pbest_pos, **kwargs)
+            self.swarm.current_cost = objective_func(
+                self.swarm.position, **kwargs
+            )
+            self.swarm.pbest_cost = objective_func(
+                self.swarm.pbest_pos, **kwargs
+            )
             self.swarm.pbest_pos, self.swarm.pbest_cost = compute_pbest(
                 self.swarm
             )
             best_cost_yet_found = self.swarm.best_cost
             # If the topology is a ring topology just use the local minimum
-            if isinstance(self.top, Ring) and not isinstance(self.top, VonNeumann):
+            if isinstance(self.top, Ring) and not isinstance(
+                self.top, VonNeumann
+            ):
                 # Update gbest from neighborhood
                 self.swarm.best_pos, self.swarm.best_cost = self.top.compute_gbest(
                     self.swarm, self.p, self.k
@@ -279,10 +291,12 @@ def optimize(self, objective_func, iters, print_step=1, verbose=1, **kwargs):
             # Print to console
             if i % print_step == 0:
                 cli_print(
-                    "Iteration {}/{}, cost: {}".format(i + 1, iters, self.swarm.best_cost),
+                    "Iteration {}/{}, cost: {}".format(
+                        i + 1, iters, self.swarm.best_cost
+                    ),
                     verbose,
                     2,
-                    logger=self.logger
+                    logger=self.logger,
                 )
             # Save to history
             hist = self.ToHistory(
@@ -290,7 +304,7 @@ def optimize(self, objective_func, iters, print_step=1, verbose=1, **kwargs):
                 mean_pbest_cost=np.mean(self.swarm.pbest_cost),
                 mean_neighbor_cost=self.swarm.best_cost,
                 position=self.swarm.position,
-                velocity=self.swarm.velocity
+                velocity=self.swarm.velocity,
             )
             self._populate_history(hist)
             # Verify stop criteria based on the relative acceptable cost ftol
@@ -314,4 +328,4 @@ def optimize(self, objective_func, iters, print_step=1, verbose=1, **kwargs):
         end_report(
             final_best_cost, final_best_pos, verbose, logger=self.logger
         )
-        return(final_best_cost, final_best_pos)
+        return (final_best_cost, final_best_pos)
diff --git a/pyswarms/single/global_best.py b/pyswarms/single/global_best.py
index 36bc812e..dbbc35ba 100644
--- a/pyswarms/single/global_best.py
+++ b/pyswarms/single/global_best.py
@@ -131,7 +131,9 @@ def __init__(
         # Initialize the topology
         self.top = Star()
 
-    def optimize(self, objective_func, iters, print_step=1, verbose=1, **kwargs):
+    def optimize(
+        self, objective_func, iters, print_step=1, verbose=1, **kwargs
+    ):
         """Optimize the swarm for a number of iterations
 
         Performs the optimization to evaluate the objective
@@ -156,13 +158,21 @@ def optimize(self, objective_func, iters, print_step=1, verbose=1, **kwargs):
             the global best cost and the global best position.
         """
 
-        cli_print("Arguments Passed to Objective Function: {}".format(kwargs),
-                  verbose, 2, logger=self.logger)
+        cli_print(
+            "Arguments Passed to Objective Function: {}".format(kwargs),
+            verbose,
+            2,
+            logger=self.logger,
+        )
 
         for i in range(iters):
             # Compute cost for current position and personal best
-            self.swarm.current_cost = objective_func(self.swarm.position, **kwargs)
-            self.swarm.pbest_cost = objective_func(self.swarm.pbest_pos, **kwargs)
+            self.swarm.current_cost = objective_func(
+                self.swarm.position, **kwargs
+            )
+            self.swarm.pbest_cost = objective_func(
+                self.swarm.pbest_pos, **kwargs
+            )
             self.swarm.pbest_pos, self.swarm.pbest_cost = compute_pbest(
                 self.swarm
             )
@@ -175,7 +185,9 @@ def optimize(self, objective_func, iters, print_step=1, verbose=1, **kwargs):
             # Print to console
             if i % print_step == 0:
                 cli_print(
-                    "Iteration {}/{}, cost: {}".format(i + 1, iters, self.swarm.best_cost),
+                    "Iteration {}/{}, cost: {}".format(
+                        i + 1, iters, self.swarm.best_cost
+                    ),
                     verbose,
                     2,
                     logger=self.logger,
diff --git a/pyswarms/single/local_best.py b/pyswarms/single/local_best.py
index 8e64b041..06cb8ad2 100644
--- a/pyswarms/single/local_best.py
+++ b/pyswarms/single/local_best.py
@@ -113,7 +113,7 @@ def __init__(
         center=1.00,
         ftol=-np.inf,
         init_pos=None,
-        static=False
+        static=False,
     ):
         """Initialize the swarm
 
@@ -178,7 +178,9 @@ def __init__(
         # Initialize the topology
         self.top = Ring(static=static)
 
-    def optimize(self, objective_func, iters, print_step=1, verbose=1, **kwargs):
+    def optimize(
+        self, objective_func, iters, print_step=1, verbose=1, **kwargs
+    ):
         """Optimize the swarm for a number of iterations
 
         Performs the optimization to evaluate the objective
@@ -203,13 +205,21 @@ def optimize(self, objective_func, iters, print_step=1, verbose=1, **kwargs):
             the local best cost and the local best position among the
             swarm.
         """
-        cli_print("Arguments Passed to Objective Function: {}".format(kwargs),
-                  verbose, 2, logger=self.logger)
+        cli_print(
+            "Arguments Passed to Objective Function: {}".format(kwargs),
+            verbose,
+            2,
+            logger=self.logger,
+        )
 
         for i in range(iters):
             # Compute cost for current position and personal best
-            self.swarm.current_cost = objective_func(self.swarm.position, **kwargs)
-            self.swarm.pbest_cost = objective_func(self.swarm.pbest_pos, **kwargs)
+            self.swarm.current_cost = objective_func(
+                self.swarm.position, **kwargs
+            )
+            self.swarm.pbest_cost = objective_func(
+                self.swarm.pbest_pos, **kwargs
+            )
             self.swarm.pbest_pos, self.swarm.pbest_cost = compute_pbest(
                 self.swarm
             )
@@ -221,7 +231,9 @@ def optimize(self, objective_func, iters, print_step=1, verbose=1, **kwargs):
             # Print to console
             if i % print_step == 0:
                 cli_print(
-                    "Iteration {}/{}, cost: {}".format(i + 1, iters, np.min(self.swarm.best_cost)),
+                    "Iteration {}/{}, cost: {}".format(
+                        i + 1, iters, np.min(self.swarm.best_cost)
+                    ),
                     verbose,
                     2,
                     logger=self.logger,
diff --git a/pyswarms/utils/__init__.py b/pyswarms/utils/__init__.py
index feb2c0bd..ea80fdaf 100644
--- a/pyswarms/utils/__init__.py
+++ b/pyswarms/utils/__init__.py
@@ -1 +1,5 @@
 """ Pacakge for various utilities """
+
+from .reporter.reporter import Reporter
+
+__all__ = ["Reporter"]
diff --git a/pyswarms/utils/plotters/formatters.py b/pyswarms/utils/plotters/formatters.py
index 78e1173b..027c14a5 100644
--- a/pyswarms/utils/plotters/formatters.py
+++ b/pyswarms/utils/plotters/formatters.py
@@ -62,11 +62,11 @@ class Designer(object):
         default=["x-axis", "y-axis", "z-axis"],
     )
     limits = attrib(
-        validator=instance_of((list, tuple)), default=[(-1, 1), (-1, 1), (-1, 1)]
+        validator=instance_of((list, tuple)),
+        default=[(-1, 1), (-1, 1), (-1, 1)],
     )
     colormap = attrib(
-        validator=instance_of(colors.Colormap),
-        default=cm.viridis,
+        validator=instance_of(colors.Colormap), default=cm.viridis
     )
 
 
diff --git a/pyswarms/utils/reporter/__init__.py b/pyswarms/utils/reporter/__init__.py
new file mode 100644
index 00000000..15390076
--- /dev/null
+++ b/pyswarms/utils/reporter/__init__.py
@@ -0,0 +1,5 @@
+"""Reporter module"""
+
+from .reporter import Reporter
+
+__all__ = ["Reporter"]
diff --git a/pyswarms/utils/reporter/reporter.py b/pyswarms/utils/reporter/reporter.py
new file mode 100644
index 00000000..61bffe6e
--- /dev/null
+++ b/pyswarms/utils/reporter/reporter.py
@@ -0,0 +1,182 @@
+# -*- coding: utf-8 -*-
+import os
+import yaml
+import pprint
+import logging
+
+
+class Reporter(object):
+    """A Reporter object that abstracts various logging capabilities
+
+    To set-up a Reporter, simply perform the following tasks:
+
+    .. code-block:: python
+
+        from pyswarms.utils import Reporter
+
+        rep = Reporter()
+        rep.log("Here's my message", lvl=20)
+
+    This will set-up a reporter with a default configuration that
+    logs to a file, `report.log`, on the current working directory.
+    You can change the log path by passing a string to the `log_path`
+    parameter:
+
+    .. code-block:: python
+
+        from pyswarms.utils import Reporter
+
+        rep = Reporter(log_path="/path/to/log/file.log")
+        rep.log("Here's my message", lvl=20)
+
+    If you are working on a module and you have an existing logger,
+    you can pass that logger instance during initialization:
+
+    .. code-block:: python
+
+        # mymodule.py
+        from pyswarms.utils import Reporter
+
+        # An existing logger in a module
+        logger = logging.getLogger(__name__)
+        rep = Reporter(logger=logger)
+
+    Lastly, if you have your own logger configuration (YAML file),
+    then simply pass that to the `config_path` parameter. This
+    overrides the default configuration (including `log_path`):
+
+    .. code-block:: python
+
+        from pyswarms.utils import Reporter
+
+        rep = Reporter(config_path="/path/to/config/file.yml")
+        rep.log("Here's my message", lvl=20)
+
+    """
+
+    def __init__(
+        self, log_path=None, config_path=None, logger=None, printer=None
+    ):
+        """Initialize the reporter
+
+        Attributes
+        ----------
+        log_path : str (default is :code:`None`)
+            Sets the default log path (overriden when :code:`path` is given to
+            :code:`_setup_logger()`)
+        config_path : str (default is :code:`None`)
+            Sets the configuration path for custom loggers
+        logger : logging.Logger (default is :code:`None`)
+            The logger object. By default, it creates a new :code:`Logger`
+            instance
+        printer : pprint.PrettyPrinter (default is :code:`None`)
+            A printer object. By default, it creates a :code:`PrettyPrinter`
+            instance with default values
+        """
+        self.logger = logger or logging.getLogger(__name__)
+        self.printer = printer or pprint.PrettyPrinter()
+        self.log_path = log_path or (os.getcwd() + "/report.log")
+        self._env_key = "LOG_CFG"
+        self._default_config = {
+            "version": 1,
+            "disable_existing_loggers": False,
+            "formatters": {
+                "standard": {
+                    "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+                }
+            },
+            "handlers": {
+                "default": {
+                    "level": "INFO",
+                    "class": "logging.StreamHandler",
+                    "formatter": "standard",
+                },
+                "file_default": {
+                    "level": "INFO",
+                    "formatter": "standard",
+                    "class": "logging.handlers.RotatingFileHandler",
+                    "filename": self.log_path,
+                    "encoding": "utf8",
+                    "maxBytes": 10485760,
+                    "backupCount": 20,
+                },
+            },
+            "loggers": {
+                "": {
+                    "handlers": ["default", "file_default"],
+                    "level": "INFO",
+                    "propagate": True,
+                }
+            },
+        }
+        self._setup_logger(config_path)
+
+    def log(self, msg, lvl=20, *args, **kwargs):
+        """Log a message within a set level
+
+        This method abstracts the logging.Logger.log() method. We use this
+        method during major state changes, errors, or critical events during
+        the optimization run.
+
+        You can check logging levels on this `link`_. In essence, DEBUG is 10,
+        INFO is 20, WARNING is 30, ERROR is 40, and CRITICAL is 50.
+
+        .. link: https://docs.python.org/3/library/logging.html#logging-levels
+
+        Parameters
+        ----------
+        msg : str
+            Message to be logged
+        lvl : int (default is 20)
+            Logging level
+        """
+        self.logger.log(lvl, msg, *args, **kwargs)
+
+    def print(self, msg, verbosity, threshold=0):
+        """Print a message into console
+
+        This method can be called during non-system calls or minor state
+        changes. In practice, we call this method when reporting the cost
+        on a given timestep.
+
+        Parameters
+        ----------
+        msg : str
+            Message to be printed
+        verbosity : int
+            Verbosity parameter, prints message when it's greater than the
+            threshold
+        threshold : int (default is 0)
+            Threshold parameer, prints message when it's lesser than the
+            verbosity
+        """
+        if verbosity > threshold:
+            self.printer.pprint(msg)
+        else:
+            pass
+
+    def _setup_logger(self, path=None):
+        """Set-up the logger with default values
+
+        This method is called right after initializing the Reporter module.
+        If no path is supplied, then it loads a default configuration.
+        You can view the defaults via the Reporter._default_config attribute.
+
+
+        Parameters
+        ----------
+        path : str
+            Path to a YAML configuration. If not supplied, uses
+            a default config.
+        """
+        value = path or os.getenv(self._env_key, None)
+        try:
+            with open(path, "rt") as f:
+                config = yaml.safe_load(f.read())
+            logging.config.dictConfig(config)
+        except (TypeError, FileNotFoundError):
+            self._load_defaults()
+
+    def _load_defaults(self):
+        """Load default logging configuration"""
+        logging.config.dictConfig(self._default_config)
diff --git a/tests/backend/topology/conftest.py b/tests/backend/topology/conftest.py
index 4aade1b1..be5a5014 100644
--- a/tests/backend/topology/conftest.py
+++ b/tests/backend/topology/conftest.py
@@ -14,6 +14,7 @@
 @pytest.fixture(scope="module")
 def swarm():
     """A contrived instance of the Swarm class at a certain timestep"""
+    # fmt: off
     attrs_at_t = {
         "position": np.array([[9.95838686e-01,  5.87433429e-04,  6.49113772e-03],
                              [1.00559609e+00,   3.96477697e-02,  7.67205397e-01],
@@ -53,4 +54,5 @@ def swarm():
         "best_pos": np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05]),
         "options": {'c1': 0.5, 'c2': 0.3, 'w': 0.9},
     }
+    # fmt: on
     return Swarm(**attrs_at_t)
diff --git a/tests/backend/topology/test_pyramid.py b/tests/backend/topology/test_pyramid.py
index 31583428..cce94e19 100644
--- a/tests/backend/topology/test_pyramid.py
+++ b/tests/backend/topology/test_pyramid.py
@@ -17,7 +17,7 @@ def test_compute_gbest_return_values(swarm, static):
     expected_pos = np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05])
     pos, cost = topology.compute_gbest(swarm)
     assert cost == pytest.approx(expected_cost)
-    assert (pos == pytest.approx(expected_pos))
+    assert pos == pytest.approx(expected_pos)
 
 
 @pytest.mark.parametrize("static", [True, False])
diff --git a/tests/backend/topology/test_random.py b/tests/backend/topology/test_random.py
index 1b12a5dc..9cc406f8 100644
--- a/tests/backend/topology/test_random.py
+++ b/tests/backend/topology/test_random.py
@@ -18,7 +18,7 @@ def test_update_gbest_neighborhood(swarm, k, static):
     expected_pos = np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05])
     expected_cost = 1.0002528364353296
     assert cost == pytest.approx(expected_cost)
-    assert (pos == pytest.approx(expected_pos))
+    assert pos == pytest.approx(expected_pos)
 
 
 @pytest.mark.parametrize("static", [True, False])
@@ -63,6 +63,7 @@ def test_compute_neighbors_adjacency_matrix(swarm, k, static):
     np.random.seed(1)
     topology = Random(static=static)
     adj_matrix = topology._Random__compute_neighbors(swarm, k)
+    # fmt: off
     comparison_matrix = np.array([[1, 1, 1, 0, 1, 0, 0, 0, 0, 1],
                                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
                                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
@@ -74,6 +75,7 @@ def test_compute_neighbors_adjacency_matrix(swarm, k, static):
                                   [0, 1, 1, 1, 1, 0, 1, 1, 1, 0],
                                   [1, 1, 1, 0, 1, 1, 1, 0, 0, 1]])
     assert np.allclose(adj_matrix, comparison_matrix, atol=1e-8)
+    # fmt: on
 
 
 @pytest.mark.parametrize("static", [True, False])
diff --git a/tests/backend/topology/test_ring.py b/tests/backend/topology/test_ring.py
index c7723724..9dc6e7a5 100644
--- a/tests/backend/topology/test_ring.py
+++ b/tests/backend/topology/test_ring.py
@@ -19,7 +19,7 @@ def test_update_gbest_neighborhood(swarm, p, k, static):
     expected_pos = np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05])
     expected_cost = 1.0002528364353296
     assert cost == pytest.approx(expected_cost)
-    assert (pos == pytest.approx(expected_pos))
+    assert pos == pytest.approx(expected_pos)
 
 
 @pytest.mark.parametrize("static", [True, False])
diff --git a/tests/backend/topology/test_star.py b/tests/backend/topology/test_star.py
index 1dc72415..957ecdad 100644
--- a/tests/backend/topology/test_star.py
+++ b/tests/backend/topology/test_star.py
@@ -16,7 +16,7 @@ def test_compute_gbest_return_values(swarm):
     expected_pos = np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05])
     pos, cost = topology.compute_gbest(swarm)
     assert cost == pytest.approx(expected_cost)
-    assert (pos == pytest.approx(expected_pos))
+    assert pos == pytest.approx(expected_pos)
 
 
 @pytest.mark.parametrize("clamp", [None, (0, 1), (-1, 1)])
diff --git a/tests/backend/topology/test_von_neumann.py b/tests/backend/topology/test_von_neumann.py
index e34a9000..1e27cd9a 100644
--- a/tests/backend/topology/test_von_neumann.py
+++ b/tests/backend/topology/test_von_neumann.py
@@ -18,7 +18,7 @@ def test_update_gbest_neighborhood(swarm, p, r):
     expected_pos = np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05])
     expected_cost = 1.0002528364353296
     assert cost == pytest.approx(expected_cost)
-    assert (pos == pytest.approx(expected_pos))
+    assert pos == pytest.approx(expected_pos)
 
 
 @pytest.mark.parametrize("clamp", [None, (0, 1), (-1, 1)])
@@ -56,11 +56,12 @@ def test_neighbor_idx(swarm, p, r):
 @pytest.mark.parametrize("m", [i for i in range(9)])
 @pytest.mark.parametrize("n", [i for i in range(10)])
 def test_delannoy_numbers(m, n):
+    # fmt: off
     expected_values = np.array([1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 25,
                                 41, 61, 63, 85, 113, 129, 145, 181, 231,
                                 321, 377, 575, 681, 833, 1159, 1289,
                                 1683, 2241, 3649, 3653, 5641, 7183,
                                 8989, 13073, 19825, 40081, 48639, 75517,
                                 108545, 22363, 224143, 265729, 598417])
-    print(VonNeumann.delannoy(m, n))
+    # fmt: on
     assert VonNeumann.delannoy(m, n) in expected_values
diff --git a/tests/optimizers/conftest.py b/tests/optimizers/conftest.py
index 0d278d4f..cb78cf1f 100644
--- a/tests/optimizers/conftest.py
+++ b/tests/optimizers/conftest.py
@@ -18,7 +18,9 @@
 def general_opt_history(topology):
     """Returns a GeneralOptimizerPSO instance run for 1000 iterations for checking
     history"""
-    pso = GeneralOptimizerPSO(10, 2, {"c1": 0.5, "c2": 0.7, "w": 0.5}, topology=topology)
+    pso = GeneralOptimizerPSO(
+        10, 2, {"c1": 0.5, "c2": 0.7, "w": 0.5}, topology=topology
+    )
     pso.optimize(sphere, 1000, verbose=0)
     return pso
 
@@ -27,7 +29,9 @@ def general_opt_history(topology):
 def general_opt_reset(topology):
     """Returns a GeneralOptimizerPSO instance that has been run and reset to check
     default value"""
-    pso = GeneralOptimizerPSO(10, 2, {"c1": 0.5, "c2": 0.7, "w": 0.5}, topology=topology)
+    pso = GeneralOptimizerPSO(
+        10, 2, {"c1": 0.5, "c2": 0.7, "w": 0.5}, topology=topology
+    )
     pso.optimize(sphere, 10, verbose=0)
     pso.reset()
     return pso
@@ -97,6 +101,7 @@ def options():
     return options_
 
 
+# fmt: off
 @pytest.fixture(params=[
                 Star(),
                 Ring(static=False), Ring(static=True),
@@ -104,6 +109,7 @@ def options():
                 Random(static=False), Random(static=True),
                 VonNeumann()
                 ])
+# fmt: on
 def topology(request):
     """Parametrized topology parameter"""
     topology_ = request.param
diff --git a/tests/optimizers/test_general_optimizer.py b/tests/optimizers/test_general_optimizer.py
index 779dad88..eac1d5b2 100644
--- a/tests/optimizers/test_general_optimizer.py
+++ b/tests/optimizers/test_general_optimizer.py
@@ -108,7 +108,7 @@ def test_invalid_r_or_p_values(options):
     [
         {"c1": 0.5, "c2": 0.7, "w": 0.5, "k": -1},
         {"c1": 0.5, "c2": 0.7, "w": 0.5, "k": 6},
-        {"c1": 0.5, "c2": 0.7, "w": 0.5, "k": 0.5}
+        {"c1": 0.5, "c2": 0.7, "w": 0.5, "k": 0.5},
     ],
 )
 def test_invalid_k_value(options, static):
@@ -118,10 +118,7 @@ def test_invalid_k_value(options, static):
         GeneralOptimizerPSO(5, 2, options, Random(static=static))
 
 
-@pytest.mark.parametrize(
-    "topology",
-    [object(), int(), dict()]
-)
+@pytest.mark.parametrize("topology", [object(), int(), dict()])
 def test_topology_type_exception(options, topology):
     """Tests if exceptions are thrown when the topology has the wrong type"""
     with pytest.raises(TypeError):
@@ -139,7 +136,9 @@ def test_topology_type_exception(options, topology):
 def test_bounds_size_exception(bounds, options, topology):
     """Tests if exceptions are raised when bound sizes are wrong"""
     with pytest.raises(IndexError):
-        GeneralOptimizerPSO(5, 2, options=options, topology=topology, bounds=bounds)
+        GeneralOptimizerPSO(
+            5, 2, options=options, topology=topology, bounds=bounds
+        )
 
 
 @pytest.mark.parametrize(
@@ -152,7 +151,9 @@ def test_bounds_size_exception(bounds, options, topology):
 def test_bounds_maxmin_exception(bounds, options, topology):
     """Tests if the max bounds is less than min bounds and vice-versa"""
     with pytest.raises(ValueError):
-        GeneralOptimizerPSO(5, 2, options=options, topology=topology, bounds=bounds)
+        GeneralOptimizerPSO(
+            5, 2, options=options, topology=topology, bounds=bounds
+        )
 
 
 @pytest.mark.parametrize(
@@ -165,7 +166,9 @@ def test_bounds_maxmin_exception(bounds, options, topology):
 def test_bound_type_exception(bounds, options, topology):
     """Tests if exception is raised when bound type is not a tuple"""
     with pytest.raises(TypeError):
-        GeneralOptimizerPSO(5, 2, options=options, topology=topology, bounds=bounds)
+        GeneralOptimizerPSO(
+            5, 2, options=options, topology=topology, bounds=bounds
+        )
 
 
 @pytest.mark.parametrize("velocity_clamp", [(1, 1, 1), (2, 3, 1)])
@@ -173,7 +176,13 @@ def test_vclamp_shape_exception(velocity_clamp, options, topology):
     """Tests if exception is raised when velocity_clamp's size is not equal
     to 2"""
     with pytest.raises(IndexError):
-        GeneralOptimizerPSO(5, 2, velocity_clamp=velocity_clamp, options=options, topology=topology)
+        GeneralOptimizerPSO(
+            5,
+            2,
+            velocity_clamp=velocity_clamp,
+            options=options,
+            topology=topology,
+        )
 
 
 @pytest.mark.parametrize("velocity_clamp", [(3, 2), (10, 8)])
@@ -181,14 +190,22 @@ def test_vclamp_maxmin_exception(velocity_clamp, options, topology):
     """Tests if the max velocity_clamp is less than min velocity_clamp and
     vice-versa"""
     with pytest.raises(ValueError):
-        GeneralOptimizerPSO(5, 2, velocity_clamp=velocity_clamp, options=options, topology=topology)
+        GeneralOptimizerPSO(
+            5,
+            2,
+            velocity_clamp=velocity_clamp,
+            options=options,
+            topology=topology,
+        )
 
 
 @pytest.mark.parametrize("err, center", [(IndexError, [1.5, 3.2, 2.5])])
 def test_center_exception(err, center, options, topology):
     """Tests if exception is thrown when center is not a list or of different shape"""
     with pytest.raises(err):
-        GeneralOptimizerPSO(5, 2, center=center, options=options, topology=topology)
+        GeneralOptimizerPSO(
+            5, 2, center=center, options=options, topology=topology
+        )
 
 
 def test_reset_default_values(gbest_reset):
@@ -216,6 +233,8 @@ def test_training_history_shape(gbest_history, history, expected_shape):
 
 def test_ftol_effect(options, topology):
     """Test if setting the ftol breaks the optimization process accordingly"""
-    pso = GeneralOptimizerPSO(10, 2, options=options, topology=topology, ftol=1e-1)
+    pso = GeneralOptimizerPSO(
+        10, 2, options=options, topology=topology, ftol=1e-1
+    )
     pso.optimize(sphere, 2000, verbose=0)
     assert np.array(pso.cost_history).shape != (2000,)
diff --git a/tests/optimizers/test_objective_func_with_kwargs.py b/tests/optimizers/test_objective_func_with_kwargs.py
index c73442dc..169fd198 100644
--- a/tests/optimizers/test_objective_func_with_kwargs.py
+++ b/tests/optimizers/test_objective_func_with_kwargs.py
@@ -16,64 +16,66 @@ def rosenbrock_with_args(x, a, b):
     return f
 
 
-@pytest.mark.parametrize('func', [
-    rosenbrock_with_args
-])
+@pytest.mark.parametrize("func", [rosenbrock_with_args])
 def test_global_kwargs(func):
     """Tests if kwargs are passed properly to the objective function for when kwargs are present"""
 
     # setup optimizer
-    options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9, 'k': 2, 'p': 2}
+    options = {"c1": 0.5, "c2": 0.3, "w": 0.9, "k": 2, "p": 2}
 
     x_max = 10 * np.ones(2)
     x_min = -1 * x_max
     bounds = (x_min, x_max)
-    opt_ps = GlobalBestPSO(n_particles=100, dimensions=2, options=options, bounds=bounds)
+    opt_ps = GlobalBestPSO(
+        n_particles=100, dimensions=2, options=options, bounds=bounds
+    )
 
     # run it
-    cost, pos = opt_ps.optimize(func, 1000, print_step=10, verbose=3, a=1 , b=100)
+    cost, pos = opt_ps.optimize(
+        func, 1000, print_step=10, verbose=3, a=1, b=100
+    )
 
     assert np.isclose(cost, 0, rtol=1e-03)
     assert np.isclose(pos[0], 1.0, rtol=1e-03)
     assert np.isclose(pos[1], 1.0, rtol=1e-03)
 
 
-@pytest.mark.parametrize('func', [
-    rosenbrock_with_args
-])
+@pytest.mark.parametrize("func", [rosenbrock_with_args])
 def test_global_kwargs_without_named_arguments(func):
     """Tests if kwargs are passed properly to the objective function for when kwargs are present and
     other named arguments are not passed, such as print_step"""
 
     # setup optimizer
-    options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9, 'k': 2, 'p': 2}
+    options = {"c1": 0.5, "c2": 0.3, "w": 0.9, "k": 2, "p": 2}
 
     x_max = 10 * np.ones(2)
     x_min = -1 * x_max
     bounds = (x_min, x_max)
-    opt_ps = GlobalBestPSO(n_particles=100, dimensions=2, options=options, bounds=bounds)
+    opt_ps = GlobalBestPSO(
+        n_particles=100, dimensions=2, options=options, bounds=bounds
+    )
 
     # run it
-    cost, pos = opt_ps.optimize(func, 1000, verbose=3, a=1 , b=100)
+    cost, pos = opt_ps.optimize(func, 1000, verbose=3, a=1, b=100)
 
     assert np.isclose(cost, 0, rtol=1e-03)
     assert np.isclose(pos[0], 1.0, rtol=1e-03)
     assert np.isclose(pos[1], 1.0, rtol=1e-03)
 
 
-@pytest.mark.parametrize('func', [
-    rosenbrock
-])
+@pytest.mark.parametrize("func", [rosenbrock])
 def test_global_no_kwargs(func):
     """Tests if args are passed properly to the objective function for when no args are present"""
 
     # setup optimizer
-    options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9, 'k': 2, 'p': 2}
+    options = {"c1": 0.5, "c2": 0.3, "w": 0.9, "k": 2, "p": 2}
 
     x_max = 10 * np.ones(2)
     x_min = -1 * x_max
     bounds = (x_min, x_max)
-    opt_ps = GlobalBestPSO(n_particles=100, dimensions=2, options=options, bounds=bounds)
+    opt_ps = GlobalBestPSO(
+        n_particles=100, dimensions=2, options=options, bounds=bounds
+    )
 
     # run it
     cost, pos = opt_ps.optimize(func, 1000, print_step=10, verbose=3)
@@ -83,41 +85,43 @@ def test_global_no_kwargs(func):
     assert np.isclose(pos[1], 1.0, rtol=1e-03)
 
 
-@pytest.mark.parametrize('func', [
-    rosenbrock_with_args
-])
+@pytest.mark.parametrize("func", [rosenbrock_with_args])
 def test_local_kwargs(func):
     """Tests if kwargs are passed properly to the objective function for when kwargs are present"""
 
     # setup optimizer
-    options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9, 'k': 2, 'p': 2}
+    options = {"c1": 0.5, "c2": 0.3, "w": 0.9, "k": 2, "p": 2}
 
     x_max = 10 * np.ones(2)
     x_min = -1 * x_max
     bounds = (x_min, x_max)
-    opt_ps = LocalBestPSO(n_particles=100, dimensions=2, options=options, bounds=bounds)
+    opt_ps = LocalBestPSO(
+        n_particles=100, dimensions=2, options=options, bounds=bounds
+    )
 
     # run it
-    cost, pos = opt_ps.optimize(func, 1000, print_step=10, verbose=3, a=1, b=100)
+    cost, pos = opt_ps.optimize(
+        func, 1000, print_step=10, verbose=3, a=1, b=100
+    )
 
     assert np.isclose(cost, 0, rtol=1e-03)
     assert np.isclose(pos[0], 1.0, rtol=1e-03)
     assert np.isclose(pos[1], 1.0, rtol=1e-03)
 
 
-@pytest.mark.parametrize('func', [
-    rosenbrock
-])
+@pytest.mark.parametrize("func", [rosenbrock])
 def test_local_no_kwargs(func):
     """Tests if no kwargs/args are passed properly to the objective function for when kwargs are present"""
 
     # setup optimizer
-    options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9, 'k': 2, 'p': 2}
+    options = {"c1": 0.5, "c2": 0.3, "w": 0.9, "k": 2, "p": 2}
 
     x_max = 10 * np.ones(2)
     x_min = -1 * x_max
     bounds = (x_min, x_max)
-    opt_ps = LocalBestPSO(n_particles=100, dimensions=2, options=options, bounds=bounds)
+    opt_ps = LocalBestPSO(
+        n_particles=100, dimensions=2, options=options, bounds=bounds
+    )
 
     # run it
     cost, pos = opt_ps.optimize(func, iters=1000, print_step=10, verbose=3)
@@ -127,121 +131,125 @@ def test_local_no_kwargs(func):
     assert np.isclose(pos[1], 1.0, rtol=1e-03)
 
 
-@pytest.mark.parametrize('func', [
-    rosenbrock
-])
+@pytest.mark.parametrize("func", [rosenbrock])
 def test_global_uneeded_kwargs(func):
     """Tests kwargs are passed the objective function for when kwargs do not exist"""
 
     # setup optimizer
-    options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9, 'k': 2, 'p': 2}
+    options = {"c1": 0.5, "c2": 0.3, "w": 0.9, "k": 2, "p": 2}
 
     x_max = 10 * np.ones(2)
     x_min = -1 * x_max
     bounds = (x_min, x_max)
-    opt_ps = GlobalBestPSO(n_particles=100, dimensions=2, options=options, bounds=bounds)
+    opt_ps = GlobalBestPSO(
+        n_particles=100, dimensions=2, options=options, bounds=bounds
+    )
 
     # run it
     with pytest.raises(TypeError) as excinfo:
         cost, pos = opt_ps.optimize(func, 1000, print_step=10, verbose=3, a=1)
-        assert 'unexpected keyword' in str(excinfo.value)
+        assert "unexpected keyword" in str(excinfo.value)
 
 
-@pytest.mark.parametrize('func', [
-    rosenbrock_with_args
-])
+@pytest.mark.parametrize("func", [rosenbrock_with_args])
 def test_global_missed_kwargs(func):
     """Tests kwargs are passed the objective function for when kwargs do not exist"""
 
     # setup optimizer
-    options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9, 'k': 2, 'p': 2}
+    options = {"c1": 0.5, "c2": 0.3, "w": 0.9, "k": 2, "p": 2}
 
     x_max = 10 * np.ones(2)
     x_min = -1 * x_max
     bounds = (x_min, x_max)
-    opt_ps = GlobalBestPSO(n_particles=100, dimensions=2, options=options, bounds=bounds)
+    opt_ps = GlobalBestPSO(
+        n_particles=100, dimensions=2, options=options, bounds=bounds
+    )
 
     # run it
     with pytest.raises(TypeError) as excinfo:
         cost, pos = opt_ps.optimize(func, 1000, print_step=10, verbose=3, a=1)
-        assert 'missing 1 required positional argument' in str(excinfo.value)
+        assert "missing 1 required positional argument" in str(excinfo.value)
 
 
-@pytest.mark.parametrize('func', [
-    rosenbrock
-])
+@pytest.mark.parametrize("func", [rosenbrock])
 def test_local_uneeded_kwargs(func):
     """Tests kwargs are passed the objective function for when kwargs do not exist"""
 
     # setup optimizer
-    options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9, 'k': 2, 'p': 2}
+    options = {"c1": 0.5, "c2": 0.3, "w": 0.9, "k": 2, "p": 2}
 
     x_max = 10 * np.ones(2)
     x_min = -1 * x_max
     bounds = (x_min, x_max)
-    opt_ps = LocalBestPSO(n_particles=100, dimensions=2, options=options, bounds=bounds)
+    opt_ps = LocalBestPSO(
+        n_particles=100, dimensions=2, options=options, bounds=bounds
+    )
 
     # run it
     with pytest.raises(TypeError) as excinfo:
         cost, pos = opt_ps.optimize(func, 1000, print_step=10, verbose=3, a=1)
-        assert 'unexpected keyword' in str(excinfo.value)
+        assert "unexpected keyword" in str(excinfo.value)
 
 
-@pytest.mark.parametrize('func', [
-    rosenbrock_with_args
-])
+@pytest.mark.parametrize("func", [rosenbrock_with_args])
 def test_local_missed_kwargs(func):
     """Tests kwargs are passed the objective function for when kwargs do not exist"""
 
     # setup optimizer
-    options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9, 'k': 2, 'p': 2}
+    options = {"c1": 0.5, "c2": 0.3, "w": 0.9, "k": 2, "p": 2}
 
     x_max = 10 * np.ones(2)
     x_min = -1 * x_max
     bounds = (x_min, x_max)
-    opt_ps = LocalBestPSO(n_particles=100, dimensions=2, options=options, bounds=bounds)
+    opt_ps = LocalBestPSO(
+        n_particles=100, dimensions=2, options=options, bounds=bounds
+    )
 
     # run it
     with pytest.raises(TypeError) as excinfo:
         cost, pos = opt_ps.optimize(func, 1000, print_step=10, verbose=3, a=1)
-        assert 'missing 1 required positional argument' in str(excinfo.value)
+        assert "missing 1 required positional argument" in str(excinfo.value)
 
 
-@pytest.mark.parametrize('func', [
-    rosenbrock_with_args
-])
+@pytest.mark.parametrize("func", [rosenbrock_with_args])
 def test_local_wrong_kwargs(func):
     """Tests kwargs are passed the objective function for when kwargs do not exist"""
 
     # setup optimizer
-    options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9, 'k': 2, 'p': 2}
+    options = {"c1": 0.5, "c2": 0.3, "w": 0.9, "k": 2, "p": 2}
 
     x_max = 10 * np.ones(2)
     x_min = -1 * x_max
     bounds = (x_min, x_max)
-    opt_ps = LocalBestPSO(n_particles=100, dimensions=2, options=options, bounds=bounds)
+    opt_ps = LocalBestPSO(
+        n_particles=100, dimensions=2, options=options, bounds=bounds
+    )
 
     # run it
     with pytest.raises(TypeError) as excinfo:
-        cost, pos = opt_ps.optimize(func, 1000, print_step=10, verbose=3, c=1, d=100)
-        assert 'unexpected keyword' in str(excinfo.value)
+        cost, pos = opt_ps.optimize(
+            func, 1000, print_step=10, verbose=3, c=1, d=100
+        )
+        assert "unexpected keyword" in str(excinfo.value)
 
 
-@pytest.mark.parametrize('func', [
-    rosenbrock_with_args
-])
+@pytest.mark.parametrize("func", [rosenbrock_with_args])
 def test_global_wrong_kwargs(func):
     """Tests kwargs are passed the objective function for when kwargs do not exist"""
 
     # setup optimizer
-    options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9, 'k': 2, 'p': 2}
+    options = {"c1": 0.5, "c2": 0.3, "w": 0.9, "k": 2, "p": 2}
 
     x_max = 10 * np.ones(2)
     x_min = -1 * x_max
     bounds = (x_min, x_max)
-    opt_ps = GlobalBestPSO(n_particles=100, dimensions=2, options=options, bounds=bounds)
+    opt_ps = GlobalBestPSO(
+        n_particles=100, dimensions=2, options=options, bounds=bounds
+    )
 
     # run it
     with pytest.raises(TypeError) as excinfo:
-        cost, pos = opt_ps.optimize(func, 1000, print_step=10, verbose=3, c=1, d=100)
-        assert 'unexpected keyword' in str(excinfo.value)
+        cost, pos = opt_ps.optimize(
+            func, 1000, print_step=10, verbose=3, c=1, d=100
+        )
+        assert "unexpected keyword" in str(excinfo.value)
diff --git a/tests/utils/functions/test_singleobj_bounds.py b/tests/utils/functions/test_singleobj_bounds.py
index 5390f7f9..17fa9379 100644
--- a/tests/utils/functions/test_singleobj_bounds.py
+++ b/tests/utils/functions/test_singleobj_bounds.py
@@ -33,7 +33,6 @@
 }
 
 
-
 def test_ackley_bound_fail(outbound):
     """Test ackley bound exception"""
     with pytest.raises(ValueError):
diff --git a/tests/utils/functions/test_singleobj_dims.py b/tests/utils/functions/test_singleobj_dims.py
index 9ab0e38d..a7f7c328 100644
--- a/tests/utils/functions/test_singleobj_dims.py
+++ b/tests/utils/functions/test_singleobj_dims.py
@@ -41,6 +41,7 @@ def test_easom_dim_fail(outdim):
     with pytest.raises(IndexError):
         fx.easom(outdim)
 
+
 def test_goldstein_dim_fail(outdim):
     """Test goldstein dim exception"""
     with pytest.raises(IndexError):
diff --git a/tests/utils/functions/test_singleobj_return.py b/tests/utils/functions/test_singleobj_return.py
index 3aced1b5..e3cf8c74 100644
--- a/tests/utils/functions/test_singleobj_return.py
+++ b/tests/utils/functions/test_singleobj_return.py
@@ -10,9 +10,6 @@
 from pyswarms.utils.functions import single_obj as fx
 
 
-
-
-
 def test_ackley_output(common_minima):
     """Tests ackley function output."""
     assert np.isclose(fx.ackley(common_minima), np.zeros(3)).all()
@@ -20,45 +17,38 @@ def test_ackley_output(common_minima):
 
 def test_beale_output(common_minima2):
     """Tests beale function output."""
-    assert np.isclose(
-        fx.beale([3, 0.5] * common_minima2), np.zeros(3)
-    ).all()
+    assert np.isclose(fx.beale([3, 0.5] * common_minima2), np.zeros(3)).all()
 
 
 def test_booth_output(common_minima2):
     """Test booth function output."""
-    assert np.isclose(
-        fx.booth([1, 3] * common_minima2), np.zeros(3)
-    ).all()
+    assert np.isclose(fx.booth([1, 3] * common_minima2), np.zeros(3)).all()
 
 
 def test_bukin6_output(common_minima2):
     """Test bukin function output."""
-    assert np.isclose(
-        fx.bukin6([-10, 1] * common_minima2), np.zeros(3)
-    ).all()
+    assert np.isclose(fx.bukin6([-10, 1] * common_minima2), np.zeros(3)).all()
 
 
 @pytest.mark.parametrize(
     "x",
     [
-        np.array([[1.34941, -1.34941],
-                  [1.34941, 1.34941],
-                  [-1.34941, 1.34941],
-                  [-1.34941, -1.34941]])
+        np.array(
+            [
+                [1.34941, -1.34941],
+                [1.34941, 1.34941],
+                [-1.34941, 1.34941],
+                [-1.34941, -1.34941],
+            ]
+        )
     ],
 )
 @pytest.mark.parametrize(
-    "minima",
-    [
-        np.array([-2.06261, -2.06261, -2.06261, -2.06261])
-    ],
+    "minima", [np.array([-2.06261, -2.06261, -2.06261, -2.06261])]
 )
 def test_crossintray_output(x, minima):
     """Tests crossintray function output."""
-    assert np.isclose(
-        fx.crossintray(x), minima
-    ).all()
+    assert np.isclose(fx.crossintray(x), minima).all()
 
 
 def test_easom_output(common_minima2):
@@ -71,7 +61,8 @@ def test_easom_output(common_minima2):
 def test_eggholder_output(common_minima2):
     """Tests eggholder function output."""
     assert np.isclose(
-        fx.eggholder([512, 404.3219] * common_minima2), (-959.6407 * np.ones(3))
+        fx.eggholder([512, 404.3219] * common_minima2),
+        (-959.6407 * np.ones(3)),
     ).all()
 
 
@@ -85,39 +76,40 @@ def test_goldstein_output(common_minima2):
 @pytest.mark.parametrize(
     "x",
     [
-        np.array([[3.0, 2.0],
-                  [-2.805118, 3.131312],
-                  [-3.779310, -3.283186],
-                  [3.584428, -1.848126]])
+        np.array(
+            [
+                [3.0, 2.0],
+                [-2.805118, 3.131312],
+                [-3.779310, -3.283186],
+                [3.584428, -1.848126],
+            ]
+        )
     ],
 )
 def test_himmelblau_output(x):
     """Tests himmelblau function output."""
-    assert np.isclose(
-        fx.himmelblau(x), np.zeros(4)
-    ).all()
+    assert np.isclose(fx.himmelblau(x), np.zeros(4)).all()
 
 
 @pytest.mark.parametrize(
     "x",
     [
-        np.array([[8.05502, 9.66459],
-                  [-8.05502, 9.66459],
-                  [8.05502, -9.66459],
-                  [-8.05502, -9.66459]])
+        np.array(
+            [
+                [8.05502, 9.66459],
+                [-8.05502, 9.66459],
+                [8.05502, -9.66459],
+                [-8.05502, -9.66459],
+            ]
+        )
     ],
 )
 @pytest.mark.parametrize(
-    "minima",
-    [
-        np.array([-19.2085, -19.2085, -19.2085, -19.2085])
-    ],
+    "minima", [np.array([-19.2085, -19.2085, -19.2085, -19.2085])]
 )
 def test_holdertable_output(x, minima):
     """Tests holdertable function output."""
-    assert np.isclose(
-        fx.holdertable(x), minima
-    ).all()
+    assert np.isclose(fx.holdertable(x), minima).all()
 
 
 def test_levi_output(common_minima2):
diff --git a/travis_pypi_setup.py b/travis_pypi_setup.py
index 844491f9..a0354fde 100644
--- a/travis_pypi_setup.py
+++ b/travis_pypi_setup.py
@@ -20,9 +20,10 @@
     from urllib.request import urlopen
 
 
-GITHUB_REPO = 'ljvmiranda921/pyswarms'
+GITHUB_REPO = "ljvmiranda921/pyswarms"
 TRAVIS_CONFIG_FILE = os.path.join(
-    os.path.dirname(os.path.abspath(__file__)), '.travis.yml')
+    os.path.dirname(os.path.abspath(__file__)), ".travis.yml"
+)
 
 
 def load_key(pubkey):
@@ -37,7 +38,7 @@ def load_key(pubkey):
         return load_pem_public_key(pubkey.encode(), default_backend())
     except ValueError:
         # workaround for https://github.com/travis-ci/travis-api/issues/196
-        pubkey = pubkey.replace('BEGIN RSA', 'BEGIN').replace('END RSA', 'END')
+        pubkey = pubkey.replace("BEGIN RSA", "BEGIN").replace("END RSA", "END")
         return load_pem_public_key(pubkey.encode(), default_backend())
 
 
@@ -57,13 +58,13 @@ def fetch_public_key(repo):
 
     Travis API docs: http://docs.travis-ci.com/api/#repository-keys
     """
-    keyurl = 'https://api.travis-ci.org/repos/{0}/key'.format(repo)
+    keyurl = "https://api.travis-ci.org/repos/{0}/key".format(repo)
     data = json.loads(urlopen(keyurl).read().decode())
-    if 'key' not in data:
+    if "key" not in data:
         errmsg = "Could not find public key for repo: {}.\n".format(repo)
         errmsg += "Have you already added your GitHub repo to Travis?"
         raise ValueError(errmsg)
-    return data['key']
+    return data["key"]
 
 
 def prepend_line(filepath, line):
@@ -73,7 +74,7 @@ def prepend_line(filepath, line):
 
     lines.insert(0, line)
 
-    with open(filepath, 'w') as f:
+    with open(filepath, "w") as f:
         f.writelines(lines)
 
 
@@ -85,7 +86,7 @@ def load_yaml_config(filepath):
 
 def save_yaml_config(filepath, config):
     """Save yaml config file at the given path."""
-    with open(filepath, 'w') as f:
+    with open(filepath, "w") as f:
         yaml.dump(config, f, default_flow_style=False)
 
 
@@ -93,12 +94,14 @@ def update_travis_deploy_password(encrypted_password):
     """Put `encrypted_password` into the deploy section of .travis.yml."""
     config = load_yaml_config(TRAVIS_CONFIG_FILE)
 
-    config['deploy']['password'] = dict(secure=encrypted_password)
+    config["deploy"]["password"] = dict(secure=encrypted_password)
 
     save_yaml_config(TRAVIS_CONFIG_FILE, config)
 
-    line = ('# This file was autogenerated and will overwrite'
-            ' each time you run travis_pypi_setup.py\n')
+    line = (
+        "# This file was autogenerated and will overwrite"
+        " each time you run travis_pypi_setup.py\n"
+    )
     prepend_line(TRAVIS_CONFIG_FILE, line)
 
 
@@ -110,18 +113,23 @@ def main(args):
     password.
     """
     public_key = fetch_public_key(args.repo)
-    password = args.password or getpass('PyPI password: ')
+    password = args.password or getpass("PyPI password: ")
     update_travis_deploy_password(encrypt(public_key, password.encode()))
     print("Wrote encrypted password to .travis.yml -- you're ready to deploy")
 
 
-if '__main__' == __name__:
+if "__main__" == __name__:
     import argparse
+
     parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument('--repo', default=GITHUB_REPO,
-                        help='GitHub repo (default: %s)' % GITHUB_REPO)
-    parser.add_argument('--password',
-                        help='PyPI password (will prompt if not provided)')
+    parser.add_argument(
+        "--repo",
+        default=GITHUB_REPO,
+        help="GitHub repo (default: %s)" % GITHUB_REPO,
+    )
+    parser.add_argument(
+        "--password", help="PyPI password (will prompt if not provided)"
+    )
 
     args = parser.parse_args()
     main(args)