diff --git a/docs/api/_pyswarms.utils.rst b/docs/api/_pyswarms.utils.rst
index bf8dd659..131289e2 100644
--- a/docs/api/_pyswarms.utils.rst
+++ b/docs/api/_pyswarms.utils.rst
@@ -10,3 +10,4 @@ functionalities.
    pyswarms.utils.functions
    pyswarms.utils.search
    pyswarms.utils.plotters
+   pyswarms.utils.reporter
diff --git a/docs/api/pyswarms.utils.reporter.rst b/docs/api/pyswarms.utils.reporter.rst
new file mode 100644
index 00000000..ba18e082
--- /dev/null
+++ b/docs/api/pyswarms.utils.reporter.rst
@@ -0,0 +1,10 @@
+pyswarms.utils.reporter package
+================================
+
+.. automodule:: pyswarms.utils.reporter.reporter
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :private-members:
+   :special-members: __init__
+
diff --git a/pyswarms/backend/generators.py b/pyswarms/backend/generators.py
index d475fbab..91f7dbf6 100644
--- a/pyswarms/backend/generators.py
+++ b/pyswarms/backend/generators.py
@@ -9,12 +9,15 @@
 
 """
 
-# Import modules
+import logging
+
 import numpy as np
 
-# Import from package
+from ..utils.reporter import Reporter
 from .swarms import Swarm
 
+rep = Reporter(logger=logging.getLogger(__name__))
+
 
 def generate_swarm(
     n_particles, dimensions, bounds=None, center=1.00, init_pos=None
@@ -67,7 +70,12 @@ def generate_swarm(
                 low=min_bounds, high=max_bounds, size=(n_particles, dimensions)
             )
     except ValueError:
+        rep.logger.exception(
+            "Please check the size and value of bounds and dimensions"
+        )
         raise
+    except TypeError:
+        rep.logger.exception("Invalid input type!")
     else:
         return pos
 
@@ -103,7 +111,11 @@ def generate_discrete_swarm(
                 size=(n_particles, dimensions)
             ).argsort(axis=1)
     except ValueError:
-        raise
+        rep.logger.exception(
+            "Please check the size and value of bounds and dimensions"
+        )
+    except TypeError:
+        rep.logger.exception("Invalid input type!")
     else:
         return pos
 
@@ -132,8 +144,12 @@ def generate_velocity(n_particles, dimensions, clamp=None):
         velocity = (max_velocity - min_velocity) * np.random.random_sample(
             size=(n_particles, dimensions)
         ) + min_velocity
-    except (ValueError, TypeError):
-        raise
+    except ValueError:
+        rep.logger.exception(
+            "Please check the size and value of clamp and dimensions"
+        )
+    except TypeError:
+        rep.logger.exception("Invalid input type!")
     else:
         return velocity
 
diff --git a/pyswarms/backend/operators.py b/pyswarms/backend/operators.py
index fb42ecd2..7714f241 100644
--- a/pyswarms/backend/operators.py
+++ b/pyswarms/backend/operators.py
@@ -8,14 +8,13 @@
 to specify how the swarm will behave.
 """
 
-# Import from stdlib
 import logging
 
-# Import modules
 import numpy as np
 
-# Create a logger
-logger = logging.getLogger(__name__)
+from ..utils.reporter import Reporter
+
+rep = Reporter(logger=logging.getLogger(__name__))
 
 
 def compute_pbest(swarm):
@@ -67,9 +66,9 @@ def compute_pbest(swarm):
             ~mask_cost, swarm.pbest_cost, swarm.current_cost
         )
     except AttributeError:
-        msg = "Please pass a Swarm class. You passed {}".format(type(swarm))
-        logger.error(msg)
-        raise
+        rep.logger.exception(
+            "Please pass a Swarm class. You passed {}".format(type(swarm))
+        )
     else:
         return (new_pbest_pos, new_pbest_cost)
 
@@ -137,13 +136,11 @@ def compute_velocity(swarm, clamp):
             )
             updated_velocity = np.where(~mask, swarm.velocity, temp_velocity)
     except AttributeError:
-        msg = "Please pass a Swarm class. You passed {}".format(type(swarm))
-        logger.error(msg)
-        raise
+        rep.logger.exception(
+            "Please pass a Swarm class. You passed {}".format(type(swarm))
+        )
     except KeyError:
-        msg = "Missing keyword in swarm.options"
-        logger.error(msg)
-        raise
+        rep.logger.exception("Missing keyword in swarm.options")
     else:
         return updated_velocity
 
@@ -187,8 +184,8 @@ def compute_position(swarm, bounds):
             temp_position = np.where(~mask, swarm.position, temp_position)
         position = temp_position
     except AttributeError:
-        msg = "Please pass a Swarm class. You passed {}".format(type(swarm))
-        logger.error(msg)
-        raise
+        rep.logger.exception(
+            "Please pass a Swarm class. You passed {}".format(type(swarm))
+        )
     else:
         return position
diff --git a/pyswarms/backend/swarms.py b/pyswarms/backend/swarms.py
index a6d4db06..71a6d730 100644
--- a/pyswarms/backend/swarms.py
+++ b/pyswarms/backend/swarms.py
@@ -8,9 +8,8 @@
 as input to most backend cases.
 """
 
-# Import modules
 import numpy as np
-from attr import attrs, attrib
+from attr import attrib, attrs
 from attr.validators import instance_of
 
 
diff --git a/pyswarms/backend/topology/base.py b/pyswarms/backend/topology/base.py
index 38a670a7..63129ad5 100644
--- a/pyswarms/backend/topology/base.py
+++ b/pyswarms/backend/topology/base.py
@@ -12,12 +12,10 @@
 :mod:`pyswarms.backend.swarms.Swarm` module.
 """
 
-# Import from stdlib
 import abc
 import logging
 
-# Import from package
-from ...utils.console_utils import cli_print
+from ...utils.reporter import Reporter
 
 
 class Topology(abc.ABC):
@@ -25,19 +23,17 @@ def __init__(self, static, **kwargs):
         """Initializes the class"""
 
         # Initialize logger
-        self.logger = logging.getLogger(__name__)
+        self.rep = Reporter(logger=logging.getLogger(__name__))
 
         # Initialize attributes
         self.static = static
         self.neighbor_idx = None
 
         if self.static:
-            cli_print(
-                "Running on `dynamic` topology, neighbors are updated regularly."
-                "Set `static=True` for fixed neighbors.",
-                1,
-                0,
-                self.logger,
+            self.rep.log(
+                "Running on `dynamic` topology,"
+                "set `static=True` for fixed neighbors.",
+                lvl=10,
             )
 
     @abc.abstractmethod
diff --git a/pyswarms/backend/topology/pyramid.py b/pyswarms/backend/topology/pyramid.py
index f1af7713..53b0fea4 100644
--- a/pyswarms/backend/topology/pyramid.py
+++ b/pyswarms/backend/topology/pyramid.py
@@ -6,20 +6,15 @@
 This class implements a pyramid topology. In this topology, the particles are connected by N-dimensional simplices.
 """
 
-# Import from stdlib
 import logging
 
-# Import modules
 import numpy as np
 from scipy.spatial import Delaunay
 
-# Import from package
 from .. import operators as ops
+from ...utils.reporter import Reporter
 from .base import Topology
 
-# Create a logger
-logger = logging.getLogger(__name__)
-
 
 class Pyramid(Topology):
     def __init__(self, static=False):
@@ -32,6 +27,7 @@ def __init__(self, static=False):
             is static or dynamic
         """
         super(Pyramid, self).__init__(static)
+        self.rep = Reporter(logger=logging.getLogger(__name__))
 
     def compute_gbest(self, swarm):
         """Update the global best using a pyramid neighborhood approach
@@ -102,10 +98,9 @@ def compute_gbest(self, swarm):
                     best_neighbor[np.argmin(swarm.pbest_cost[best_neighbor])]
                 ]
         except AttributeError:
-            msg = "Please pass a Swarm class. You passed {}".format(
-                type(swarm)
+            self.rep.logger.exception(
+                "Please pass a Swarm class. You passed {}".format(type(swarm))
             )
-            logger.error(msg)
             raise
         else:
             return (best_pos, best_cost)
diff --git a/pyswarms/backend/topology/random.py b/pyswarms/backend/topology/random.py
index 91464ee6..6794af22 100644
--- a/pyswarms/backend/topology/random.py
+++ b/pyswarms/backend/topology/random.py
@@ -6,21 +6,16 @@
 This class implements a random topology. All particles are connected in a random fashion.
 """
 
-# Import from stdlib
-import logging
 import itertools
+import logging
 
-# Import modules
 import numpy as np
 from scipy.sparse.csgraph import connected_components, dijkstra
 
-# Import from package
 from .. import operators as ops
+from ...utils.reporter import Reporter
 from .base import Topology
 
-# Create a logger
-logger = logging.getLogger(__name__)
-
 
 class Random(Topology):
     def __init__(self, static=False):
@@ -32,6 +27,7 @@ def __init__(self, static=False):
             a boolean that decides whether the topology
             is static or dynamic"""
         super(Random, self).__init__(static)
+        self.rep = Reporter(logger=logging.getLogger(__name__))
 
     def compute_gbest(self, swarm, k):
         """Update the global best using a random neighborhood approach
@@ -91,10 +87,9 @@ def compute_gbest(self, swarm, k):
             ]
 
         except AttributeError:
-            msg = "Please pass a Swarm class. You passed {}".format(
-                type(swarm)
+            self.rep.logger.exception(
+                "Please pass a Swarm class. You passed {}".format(type(swarm))
             )
-            logger.error(msg)
             raise
         else:
             return (best_pos, best_cost)
diff --git a/pyswarms/backend/topology/ring.py b/pyswarms/backend/topology/ring.py
index 660899c4..54425e90 100644
--- a/pyswarms/backend/topology/ring.py
+++ b/pyswarms/backend/topology/ring.py
@@ -9,20 +9,15 @@
 optimizers.
 """
 
-# Import from stdlib
 import logging
 
-# Import modules
 import numpy as np
 from scipy.spatial import cKDTree
 
-# Import from package
 from .. import operators as ops
+from ...utils.reporter import Reporter
 from .base import Topology
 
-# Create a logger
-logger = logging.getLogger(__name__)
-
 
 class Ring(Topology):
     def __init__(self, static=False):
@@ -34,6 +29,7 @@ def __init__(self, static=False):
             a boolean that decides whether the topology
             is static or dynamic"""
         super(Ring, self).__init__(static)
+        self.rep = Reporter(logger=logging.getLogger(__name__))
 
     def compute_gbest(self, swarm, p, k):
         """Update the global best using a ring-like neighborhood approach
@@ -86,10 +82,9 @@ def compute_gbest(self, swarm, p, k):
                 best_neighbor[np.argmin(swarm.pbest_cost[best_neighbor])]
             ]
         except AttributeError:
-            msg = "Please pass a Swarm class. You passed {}".format(
-                type(swarm)
+            self.rep.logger.exception(
+                "Please pass a Swarm class. You passed {}".format(type(swarm))
             )
-            logger.error(msg)
             raise
         else:
             return (best_pos, best_cost)
diff --git a/pyswarms/backend/topology/star.py b/pyswarms/backend/topology/star.py
index 8a8f3750..b341f7d9 100644
--- a/pyswarms/backend/topology/star.py
+++ b/pyswarms/backend/topology/star.py
@@ -9,23 +9,19 @@
 optimizers.
 """
 
-# Import from stdlib
 import logging
 
-# Import modules
 import numpy as np
 
-# Import from package
 from .. import operators as ops
+from ...utils.reporter import Reporter
 from .base import Topology
 
-# Create a logger
-logger = logging.getLogger(__name__)
-
 
 class Star(Topology):
     def __init__(self):
         super(Star, self).__init__(static=True)
+        self.rep = Reporter(logger=logging.getLogger(__name__))
 
     def compute_gbest(self, swarm):
         """Update the global best using a star topology
@@ -68,10 +64,9 @@ def compute_gbest(self, swarm):
             best_pos = swarm.pbest_pos[np.argmin(swarm.pbest_cost)]
             best_cost = np.min(swarm.pbest_cost)
         except AttributeError:
-            msg = "Please pass a Swarm class. You passed {}".format(
-                type(swarm)
+            self.rep.logger.exception(
+                "Please pass a Swarm class. You passed {}".format(type(swarm))
             )
-            logger.error(msg)
             raise
         else:
             return (best_pos, best_cost)
diff --git a/pyswarms/backend/topology/von_neumann.py b/pyswarms/backend/topology/von_neumann.py
index 168ec8ec..bf33cabd 100644
--- a/pyswarms/backend/topology/von_neumann.py
+++ b/pyswarms/backend/topology/von_neumann.py
@@ -6,18 +6,16 @@
 This class implements a Von Neumann topology.
 """
 
-# Import from stdlib
 import logging
 
 from .ring import Ring
-
-# Create a logger
-logger = logging.getLogger(__name__)
+from ...utils.reporter import Reporter
 
 
 class VonNeumann(Ring):
     def __init__(self):
         super(VonNeumann, self).__init__(static=True)
+        self.rep = Reporter(logger=logging.getLogger(__name__))
 
     def compute_gbest(self, swarm, p, r):
         """Updates the global best using a neighborhood approach
diff --git a/pyswarms/base/base_discrete.py b/pyswarms/base/base_discrete.py
index 4f8ffd7f..9ac54099 100644
--- a/pyswarms/base/base_discrete.py
+++ b/pyswarms/base/base_discrete.py
@@ -28,14 +28,11 @@
 
 """
 
-import os
 import abc
-import yaml
-import logging
-import numpy as np
-import logging.config
 from collections import namedtuple
 
+import numpy as np
+
 # Import from package
 from ..backend import create_swarm
 
@@ -75,34 +72,6 @@ def assertions(self):
         if not all(key in self.options for key in ("c1", "c2", "w")):
             raise KeyError("Missing either c1, c2, or w in options")
 
-    def setup_logging(
-        self,
-        default_path="./config/logging.yaml",
-        default_level=logging.INFO,
-        env_key="LOG_CFG",
-    ):
-        """Setup logging configuration
-
-        Parameters
-        ----------
-        default_path : str (default is `./config/logging.yaml`)
-            the path where the logging configuration is stored
-        default_level: logging.LEVEL (default is `logging.INFO`)
-            the default logging level
-        env_key : str
-            the environment key for accessing the setup
-        """
-        path = default_path
-        value = os.getenv(env_key, None)
-        if value:
-            path = value
-        if os.path.exists(path):
-            with open(path, "rt") as f:
-                config = yaml.safe_load(f.read())
-            logging.config.dictConfig(config)
-        else:
-            logging.basicConfig(level=default_level)
-
     def __init__(
         self,
         n_particles,
@@ -147,7 +116,6 @@ def __init__(
             a dictionary containing the parameters for a specific
             optimization technique
         """
-        self.setup_logging()
         # Initialize primary swarm attributes
         self.n_particles = n_particles
         self.dimensions = dimensions
@@ -195,9 +163,7 @@ def _populate_history(self, hist):
         self.velocity_history.append(hist.velocity)
 
     @abc.abstractmethod
-    def optimize(
-        self, objective_func, iters, print_step=1, verbose=1, **kwargs
-    ):
+    def optimize(self, objective_func, iters, fast=False, **kwargs):
         """Optimize the swarm for a number of iterations
 
         Performs the optimization to evaluate the objective
@@ -210,10 +176,8 @@ def optimize(
             objective function to be evaluated
         iters : int
             number of iterations
-        print_step : int (the default is 1)
-            amount of steps for printing into console.
-        verbose : int (the default is 1)
-            verbosity setting.
+        fast : bool (default is False)
+            if True, time.sleep is not executed
         kwargs : dict
             arguments for objective function
 
diff --git a/pyswarms/base/base_single.py b/pyswarms/base/base_single.py
index 2cee6cad..b804593b 100644
--- a/pyswarms/base/base_single.py
+++ b/pyswarms/base/base_single.py
@@ -30,15 +30,11 @@
 :mod:`pyswarms.single.general_optimizer`: a more general PSO implementation with a custom topology
 """
 
-import os
 import abc
-import yaml
-import logging
-import numpy as np
-import logging.config
 from collections import namedtuple
 
-# Import from package
+import numpy as np
+
 from ..backend import create_swarm
 
 
@@ -108,34 +104,6 @@ def assertions(self):
         if not all(key in self.options for key in ("c1", "c2", "w")):
             raise KeyError("Missing either c1, c2, or w in options")
 
-    def setup_logging(
-        self,
-        default_path="./config/logging.yaml",
-        default_level=logging.INFO,
-        env_key="LOG_CFG",
-    ):
-        """Setup logging configuration
-
-        Parameters
-        ----------
-        default_path : str (default is `./config/logging.yaml`)
-            the path where the logging configuration is stored
-        default_level: logging.LEVEL (default is `logging.INFO`)
-            the default logging level
-        env_key : str
-            the environment key for accessing the setup
-        """
-        path = default_path
-        value = os.getenv(env_key, None)
-        if value:
-            path = value
-        if os.path.exists(path):
-            with open(path, "rt") as f:
-                config = yaml.safe_load(f.read())
-            logging.config.dictConfig(config)
-        else:
-            logging.basicConfig(level=default_level)
-
     def __init__(
         self,
         n_particles,
@@ -179,7 +147,6 @@ def __init__(
         ftol : float
             relative error in objective_func(best_pos) acceptable for convergence
         """
-        self.setup_logging()
         # Initialize primary swarm attributes
         self.n_particles = n_particles
         self.dimensions = dimensions
@@ -227,9 +194,7 @@ def _populate_history(self, hist):
         self.velocity_history.append(hist.velocity)
 
     @abc.abstractmethod
-    def optimize(
-        self, objective_func, iters, print_step=1, verbose=1, **kwargs
-    ):
+    def optimize(self, objective_func, iters, fast=False, **kwargs):
         """Optimize the swarm for a number of iterations
 
         Performs the optimization to evaluate the objective
@@ -242,10 +207,8 @@ def optimize(
             objective function to be evaluated
         iters : int
             number of iterations
-        print_step : int (the default is 1)
-            amount of steps for printing into console.
-        verbose : int (the default is 1)
-            verbosity setting.
+        fast : bool (default is False)
+            if True, time.sleep is not executed
         kwargs : dict
             arguments for objective function
 
diff --git a/pyswarms/discrete/binary.py b/pyswarms/discrete/binary.py
index 93c481e6..be4d106d 100644
--- a/pyswarms/discrete/binary.py
+++ b/pyswarms/discrete/binary.py
@@ -51,17 +51,15 @@
     Conference on Systems, Man, and Cybernetics, 1997.
 """
 
-# Import from stdlib
 import logging
+from time import sleep
 
-# Import modules
 import numpy as np
 
-# Import from package
 from ..base import DiscreteSwarmOptimizer
 from ..backend.operators import compute_pbest
 from ..backend.topology import Ring
-from ..utils.console_utils import cli_print, end_report
+from ..utils.reporter import Reporter
 
 
 class BinaryPSO(DiscreteSwarmOptimizer):
@@ -129,7 +127,7 @@ def __init__(
                     the Euclidean (or L2) distance.
         """
         # Initialize logger
-        self.logger = logging.getLogger(__name__)
+        self.rep = Reporter(logger=logging.getLogger(__name__))
         # Assign k-neighbors and p-value as attributes
         self.k, self.p = options["k"], options["p"]
         # Initialize parent class
@@ -148,10 +146,9 @@ def __init__(
         self.reset()
         # Initialize the topology
         self.top = Ring(static=False)
+        self.name = __name__
 
-    def optimize(
-        self, objective_func, iters, print_step=1, verbose=1, **kwargs
-    ):
+    def optimize(self, objective_func, iters, fast=False, **kwargs):
         """Optimize the swarm for a number of iterations
 
         Performs the optimization to evaluate the objective
@@ -163,10 +160,8 @@ def optimize(
             objective function to be evaluated
         iters : int
             number of iterations
-        print_step : int (the default is 1)
-            amount of steps for printing into console.
-        verbose : int  (the default is 1)
-            verbosity setting.
+        fast : bool (default is False)
+            if True, time.sleep is not executed
         kwargs : dict
             arguments for objective function
 
@@ -176,14 +171,14 @@ def optimize(
             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,
+        self.rep.log("Obj. func. args: {}".format(kwargs), lvl=10)
+        self.rep.log(
+            "Optimize for {} iters with {}".format(iters, self.options), lvl=20
         )
 
-        for i in range(iters):
+        for i in self.rep.pbar(iters, self.name):
+            if not fast:
+                sleep(0.01)
             # Compute cost for current position and personal best
             self.swarm.current_cost = objective_func(
                 self.swarm.position, **kwargs
@@ -200,15 +195,7 @@ def optimize(
                 self.swarm, self.p, self.k
             )
             # Print to console
-            if i % print_step == 0:
-                cli_print(
-                    "Iteration {}/{}, cost: {}".format(
-                        i + 1, iters, np.min(self.swarm.best_cost)
-                    ),
-                    verbose,
-                    2,
-                    logger=self.logger,
-                )
+            self.rep.hook(best_cost=self.swarm.best_cost)
             # Save to history
             hist = self.ToHistory(
                 best_cost=self.swarm.best_cost,
@@ -233,8 +220,11 @@ def optimize(
         # Obtain the final best_cost and the final best_position
         final_best_cost = self.swarm.best_cost.copy()
         final_best_pos = self.swarm.best_pos.copy()
-        end_report(
-            final_best_cost, final_best_pos, verbose, logger=self.logger
+        self.rep.log(
+            "Optimization finished | best cost: {}, best pos: {}".format(
+                final_best_cost, final_best_pos
+            ),
+            lvl=20,
         )
         return (final_best_cost, final_best_pos)
 
diff --git a/pyswarms/single/general_optimizer.py b/pyswarms/single/general_optimizer.py
index 27edc268..af56bb54 100644
--- a/pyswarms/single/general_optimizer.py
+++ b/pyswarms/single/general_optimizer.py
@@ -55,17 +55,16 @@
     Proceedings of the IEEE International Joint Conference on Neural
     Networks, 1995, pp. 1942-1948.
 """
-# Import from stdlib
+
 import logging
+from time import sleep
 
-# Import modules
 import numpy as np
 
-# Import from package
-from ..base import SwarmOptimizer
 from ..backend.operators import compute_pbest
-from ..backend.topology import Topology, Ring, Random, VonNeumann
-from ..utils.console_utils import cli_print, end_report
+from ..backend.topology import Random, Ring, Topology, VonNeumann
+from ..base import SwarmOptimizer
+from ..utils.reporter import Reporter
 
 
 class GeneralOptimizerPSO(SwarmOptimizer):
@@ -158,7 +157,7 @@ def __init__(
         )
 
         # Initialize logger
-        self.logger = logging.getLogger(__name__)
+        self.rep = Reporter(logger=logging.getLogger(__name__))
         # Invoke assertions
         self.assertions()
         # Initialize the resettable attributes
@@ -214,10 +213,9 @@ def __init__(
                         "Delannoy number (number of neighbours) is"
                         "between 0 and the no. of particles."
                     )
+        self.name = __name__
 
-    def optimize(
-        self, objective_func, iters, print_step=1, verbose=1, **kwargs
-    ):
+    def optimize(self, objective_func, iters, fast=False, **kwargs):
         """Optimize the swarm for a number of iterations
 
         Performs the optimization to evaluate the objective
@@ -229,10 +227,8 @@ def optimize(
             objective function to be evaluated
         iters : int
             number of iterations
-        print_step : int (default is 1)
-            amount of steps for printing into console.
-        verbose : int  (default is 1)
-            verbosity setting.
+        fast : bool (default is False)
+            if True, time.sleep is not executed
         kwargs : dict
             arguments for the objective function
 
@@ -241,15 +237,15 @@ def optimize(
         tuple
             the global best cost and the global best position.
         """
+        if not fast:
+            sleep(0.01)
 
-        cli_print(
-            "Arguments Passed to Objective Function: {}".format(kwargs),
-            verbose,
-            2,
-            logger=self.logger,
+        self.rep.log("Obj. func. args: {}".format(kwargs), lvl=10)
+        self.rep.log(
+            "Optimize for {} iters with {}".format(iters, self.options), lvl=20
         )
 
-        for i in range(iters):
+        for i in self.rep.pbar(iters, self.name):
             # Compute cost for current position and personal best
             self.swarm.current_cost = objective_func(
                 self.swarm.position, **kwargs
@@ -289,16 +285,7 @@ def optimize(
                         self.swarm
                     )
             # Print to console
-            if i % print_step == 0:
-                cli_print(
-                    "Iteration {}/{}, cost: {}".format(
-                        i + 1, iters, self.swarm.best_cost
-                    ),
-                    verbose,
-                    2,
-                    logger=self.logger,
-                )
-            # Save to history
+            self.rep.hook(best_cost=self.swarm.best_cost)
             hist = self.ToHistory(
                 best_cost=self.swarm.best_cost,
                 mean_pbest_cost=np.mean(self.swarm.pbest_cost),
@@ -325,7 +312,10 @@ def optimize(
         final_best_cost = self.swarm.best_cost.copy()
         final_best_pos = self.swarm.best_pos.copy()
         # Write report in log and return final cost and position
-        end_report(
-            final_best_cost, final_best_pos, verbose, logger=self.logger
+        self.rep.log(
+            "Optimization finished | best cost: {}, best pos: {}".format(
+                final_best_cost, final_best_pos
+            ),
+            lvl=20,
         )
         return (final_best_cost, final_best_pos)
diff --git a/pyswarms/single/global_best.py b/pyswarms/single/global_best.py
index dbbc35ba..ebe0ffb0 100644
--- a/pyswarms/single/global_best.py
+++ b/pyswarms/single/global_best.py
@@ -55,17 +55,15 @@
     Networks, 1995, pp. 1942-1948.
 """
 
-# Import from stdlib
 import logging
+from time import sleep
 
-# Import modules
 import numpy as np
 
-# Import from package
-from ..base import SwarmOptimizer
 from ..backend.operators import compute_pbest
 from ..backend.topology import Star
-from ..utils.console_utils import cli_print, end_report
+from ..base import SwarmOptimizer
+from ..utils.reporter import Reporter
 
 
 class GlobalBestPSO(SwarmOptimizer):
@@ -123,17 +121,16 @@ def __init__(
         )
 
         # Initialize logger
-        self.logger = logging.getLogger(__name__)
+        self.rep = Reporter(logger=logging.getLogger(__name__))
         # Invoke assertions
         self.assertions()
         # Initialize the resettable attributes
         self.reset()
         # Initialize the topology
         self.top = Star()
+        self.name = __name__
 
-    def optimize(
-        self, objective_func, iters, print_step=1, verbose=1, **kwargs
-    ):
+    def optimize(self, objective_func, iters, fast=False, **kwargs):
         """Optimize the swarm for a number of iterations
 
         Performs the optimization to evaluate the objective
@@ -145,10 +142,8 @@ def optimize(
             objective function to be evaluated
         iters : int
             number of iterations
-        print_step : int (default is 1)
-            amount of steps for printing into console.
-        verbose : int  (default is 1)
-            verbosity setting.
+        fast : bool (default is False)
+            if True, time.sleep is not executed
         kwargs : dict
             arguments for the objective function
 
@@ -158,14 +153,14 @@ def optimize(
             the global best cost and the global best position.
         """
 
-        cli_print(
-            "Arguments Passed to Objective Function: {}".format(kwargs),
-            verbose,
-            2,
-            logger=self.logger,
+        self.rep.log("Obj. func. args: {}".format(kwargs), lvl=10)
+        self.rep.log(
+            "Optimize for {} iters with {}".format(iters, self.options), lvl=20
         )
 
-        for i in range(iters):
+        for i in self.rep.pbar(iters, self.name):
+            if not fast:
+                sleep(0.01)
             # Compute cost for current position and personal best
             self.swarm.current_cost = objective_func(
                 self.swarm.position, **kwargs
@@ -182,16 +177,7 @@ def optimize(
                 self.swarm.best_pos, self.swarm.best_cost = self.top.compute_gbest(
                     self.swarm
                 )
-            # Print to console
-            if i % print_step == 0:
-                cli_print(
-                    "Iteration {}/{}, cost: {}".format(
-                        i + 1, iters, self.swarm.best_cost
-                    ),
-                    verbose,
-                    2,
-                    logger=self.logger,
-                )
+            self.rep.hook(best_cost=self.swarm.best_cost)
             # Save to history
             hist = self.ToHistory(
                 best_cost=self.swarm.best_cost,
@@ -219,7 +205,10 @@ def optimize(
         final_best_cost = self.swarm.best_cost.copy()
         final_best_pos = self.swarm.best_pos.copy()
         # Write report in log and return final cost and position
-        end_report(
-            final_best_cost, final_best_pos, verbose, logger=self.logger
+        self.rep.log(
+            "Optimization finished | best cost: {}, best pos: {}".format(
+                final_best_cost, final_best_pos
+            ),
+            lvl=20,
         )
         return (final_best_cost, final_best_pos)
diff --git a/pyswarms/single/local_best.py b/pyswarms/single/local_best.py
index 06cb8ad2..3716c3db 100644
--- a/pyswarms/single/local_best.py
+++ b/pyswarms/single/local_best.py
@@ -64,17 +64,15 @@
     Symposium on Micromachine and Human Science, 1995, pp. 39–43.
 """
 
-# Import from stdlib
 import logging
+from time import sleep
 
-# Import modules
 import numpy as np
 
-# Import from package
-from ..base import SwarmOptimizer
 from ..backend.operators import compute_pbest
 from ..backend.topology import Ring
-from ..utils.console_utils import cli_print, end_report
+from ..base import SwarmOptimizer
+from ..utils.reporter import Reporter
 
 
 class LocalBestPSO(SwarmOptimizer):
@@ -171,16 +169,17 @@ def __init__(
             ftol=ftol,
             init_pos=init_pos,
         )
+        # Initialize logger
+        self.rep = Reporter(logger=logging.getLogger(__name__))
         # Invoke assertions
         self.assertions()
         # Initialize the resettable attributes
         self.reset()
         # Initialize the topology
         self.top = Ring(static=static)
+        self.name = __name__
 
-    def optimize(
-        self, objective_func, iters, print_step=1, verbose=1, **kwargs
-    ):
+    def optimize(self, objective_func, iters, fast=False, **kwargs):
         """Optimize the swarm for a number of iterations
 
         Performs the optimization to evaluate the objective
@@ -192,10 +191,8 @@ def optimize(
             objective function to be evaluated
         iters : int
             number of iterations
-        print_step : int (default is 1)
-            amount of steps for printing into console.
-        verbose : int  (default is 1)
-            verbosity setting.
+        fast : bool (default is False)
+            if True, time.sleep is not executed
         kwargs : dict
             arguments for the objective function
 
@@ -205,14 +202,14 @@ def optimize(
             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,
+        self.rep.log("Obj. func. args: {}".format(kwargs), lvl=10)
+        self.rep.log(
+            "Optimize for {} iters with {}".format(iters, self.options), lvl=20
         )
 
-        for i in range(iters):
+        for i in self.rep.pbar(iters, self.name):
+            if not fast:
+                sleep(0.01)
             # Compute cost for current position and personal best
             self.swarm.current_cost = objective_func(
                 self.swarm.position, **kwargs
@@ -228,16 +225,7 @@ def optimize(
             self.swarm.best_pos, self.swarm.best_cost = self.top.compute_gbest(
                 self.swarm, self.p, self.k
             )
-            # Print to console
-            if i % print_step == 0:
-                cli_print(
-                    "Iteration {}/{}, cost: {}".format(
-                        i + 1, iters, np.min(self.swarm.best_cost)
-                    ),
-                    verbose,
-                    2,
-                    logger=self.logger,
-                )
+            self.rep.hook(best_cost=np.min(self.swarm.best_cost))
             # Save to history
             hist = self.ToHistory(
                 best_cost=self.swarm.best_cost,
@@ -264,7 +252,11 @@ def optimize(
         # Obtain the final best_cost and the final best_position
         final_best_cost = self.swarm.best_cost.copy()
         final_best_pos = self.swarm.best_pos.copy()
-        end_report(
-            final_best_cost, final_best_pos, verbose, logger=self.logger
+        # Write report in log and return final cost and position
+        self.rep.log(
+            "Optimization finished | best cost: {}, best pos: {}".format(
+                final_best_cost, final_best_pos
+            ),
+            lvl=20,
         )
         return (final_best_cost, final_best_pos)
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/console_utils.py b/pyswarms/utils/console_utils.py
deleted file mode 100644
index 22ae118d..00000000
--- a/pyswarms/utils/console_utils.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# -*- coding: utf-8 -*-
-
-""" console_utils.py: various tools for printing into console """
-
-# Import from __future__
-from __future__ import with_statement
-from __future__ import absolute_import
-from __future__ import print_function
-
-# Import modules
-
-
-def cli_print(message, verbosity, threshold, logger):
-    """Helper function to print console output
-
-    Parameters
-    ----------
-    message : str
-        the message to be printed into the console
-    verbosity : int
-        verbosity setting of the user
-    threshold : int
-        threshold for printing
-    logger : logging.getLogger
-        logger instance
-
-    """
-    if verbosity >= threshold:
-        logger.info(message)
-    else:
-        pass
-
-
-def end_report(cost, pos, verbosity, logger):
-    """Helper function to print a simple report at the end of the
-    run. This always has a threshold of 1.
-
-    Parameters
-    ----------
-    cost : float
-        final cost from the optimization procedure.
-    pos : numpy.ndarray or list
-        best position found
-    verbosity : int
-        verbosity setting of the user.
-    logger : logging.getLogger
-        logger instance
-    """
-
-    # Cuts the length of the best position if it's too long
-    if len(list(pos)) > 3:
-        out = ("[ " + 3 * "{:3f} " + "...]").format(*list(pos))
-    else:
-        out = list(pos)
-
-    template = (
-        "================================\n"
-        "Optimization finished!\n"
-        "Final cost: {:06.4f}\n"
-        "Best value: {}\n"
-    ).format(cost, out)
-    if verbosity >= 1:
-        logger.info(template)
diff --git a/pyswarms/utils/functions/single_obj.py b/pyswarms/utils/functions/single_obj.py
index 63acd877..4dfc1234 100644
--- a/pyswarms/utils/functions/single_obj.py
+++ b/pyswarms/utils/functions/single_obj.py
@@ -36,12 +36,6 @@
 - Three Hump Camel, threehump
 """
 
-# Import from __future__
-from __future__ import with_statement
-from __future__ import absolute_import
-from __future__ import print_function
-
-# Import modules
 import numpy as np
 
 
diff --git a/pyswarms/utils/plotters/formatters.py b/pyswarms/utils/plotters/formatters.py
index 027c14a5..b240ce76 100644
--- a/pyswarms/utils/plotters/formatters.py
+++ b/pyswarms/utils/plotters/formatters.py
@@ -6,9 +6,8 @@
 This module implements helpful classes to format your plots or create meshes.
 """
 
-# Import modules
 import numpy as np
-from attr import attrs, attrib
+from attr import attrib, attrs
 from attr.validators import instance_of
 from matplotlib import cm, colors
 
diff --git a/pyswarms/utils/plotters/plotters.py b/pyswarms/utils/plotters/plotters.py
index 5d92d55c..6d87e395 100644
--- a/pyswarms/utils/plotters/plotters.py
+++ b/pyswarms/utils/plotters/plotters.py
@@ -65,7 +65,6 @@
 speed of animation.
 """
 
-# Import modules
 import logging
 
 import matplotlib.pyplot as plt
@@ -73,11 +72,10 @@
 from matplotlib import animation, cm
 from mpl_toolkits.mplot3d import Axes3D
 
-# Import from package
 from .formatters import Designer, Animator
+from ..reporter import Reporter
 
-# Initialize logger
-logger = logging.getLogger(__name__)
+rep = Reporter(logger=logging.getLogger(__name__))
 
 
 def plot_cost_history(
@@ -132,6 +130,7 @@ def plot_cost_history(
         ax.set_ylabel(designer.label[1], fontsize=designer.text_fontsize)
         ax.tick_params(labelsize=designer.text_fontsize)
     except TypeError:
+        rep.logger.exception("Please check your input type")
         raise
     else:
         return ax
@@ -234,6 +233,7 @@ def plot_contour(
             repeat_delay=animator.repeat_delay,
         )
     except TypeError:
+        rep.logger.exception("Please check your input type")
         raise
     else:
         return anim
@@ -376,6 +376,7 @@ def plot_surface(
             repeat_delay=animator.repeat_delay,
         )
     except TypeError:
+        rep.logger.exception("Please check your input type")
         raise
     else:
         return anim
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..01853c5d
--- /dev/null
+++ b/pyswarms/utils/reporter/reporter.py
@@ -0,0 +1,233 @@
+# -*- coding: utf-8 -*-
+import os
+import yaml
+import pprint
+import logging
+import logging.config
+from tqdm import trange
+
+
+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._bar_fmt = "{l_bar}{bar}|{n_fmt}/{total_fmt}{postfix}"
+        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(value, "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)
+
+    def pbar(self, iters, desc=None):
+        """Create a tqdm iterable
+
+        You can use this method to create progress bars. It uses a set
+        of abstracted methods from tqdm:
+
+        .. code-block:: python
+
+            from pyswarms.utils import Reporter
+
+            rep = Reporter()
+            # Create a progress bar
+            for i in rep.pbar(100, name="Optimizer")
+                    pass
+
+        Parameters
+        ----------
+        iters : int
+            Maximum range passed to the tqdm instance
+        desc : str
+            Name of the progress bar that will be displayed
+
+        Returns
+        -------
+        tqdm._tqdm.tqdm
+            A tqdm iterable
+        """
+        self.t = trange(iters, desc=desc, bar_format=self._bar_fmt)
+        return self.t
+
+    def hook(self, *args, **kwargs):
+        """Set a hook on the progress bar
+
+        Method for creating a postfix in tqdm. In practice we use this
+        to report the best cost found during an iteration:
+
+        .. code-block:: python
+
+            from pyswarms.utils import Reporter
+
+            rep = Reporter()
+            # Create a progress bar
+            for i in rep.pbar(100, name="Optimizer")
+                    best_cost = compute()
+                    rep.hook(best_cost=best_cost)
+        """
+        self.t.set_postfix(*args, **kwargs)
diff --git a/requirements_dev.txt b/requirements_dev.txt
index a2d2ed01..cf24109b 100644
--- a/requirements_dev.txt
+++ b/requirements_dev.txt
@@ -12,7 +12,8 @@ PyYAML==3.13 # pyup: ignore
 future==0.16.0
 scipy>=0.17.0
 numpy>=1.13.0
+tqdm==4.24.0
 matplotlib>=1.3.1
 pytest==3.7.1
 attrs==18.1.0
-pre-commit==1.10.5
\ No newline at end of file
+pre-commit==1.10.5
diff --git a/setup.py b/setup.py
index 44fc25c5..f8574a13 100644
--- a/setup.py
+++ b/setup.py
@@ -17,6 +17,7 @@
     "mock==2.0.0",
     "pytest==3.6.4",
     "attrs==18.1.0",
+    "tqdm==4.24.0",
     "pre-commit",
 ]
 
@@ -31,6 +32,7 @@
     "numpy>=1.13.0",
     "matplotlib>=1.3.1",
     "mock==2.0.0",
+    "tqdm==4.24.0",
     "pytest==3.6.4",
     "attrs==18.1.0",
     "pre-commit",
diff --git a/tests/optimizers/conftest.py b/tests/optimizers/conftest.py
index cb78cf1f..58cc5e78 100644
--- a/tests/optimizers/conftest.py
+++ b/tests/optimizers/conftest.py
@@ -3,15 +3,12 @@
 
 """Fixtures for tests"""
 
-# Import modules
 import pytest
-import numpy as np
 
-# Import from package
-from pyswarms.single import GlobalBestPSO, LocalBestPSO, GeneralOptimizerPSO
+from pyswarms.backend.topology import Pyramid, Random, Ring, Star, VonNeumann
 from pyswarms.discrete import BinaryPSO
+from pyswarms.single import GeneralOptimizerPSO, GlobalBestPSO, LocalBestPSO
 from pyswarms.utils.functions.single_obj import sphere
-from pyswarms.backend.topology import Star, Ring, Pyramid, Random, VonNeumann
 
 
 @pytest.fixture(scope="module")
@@ -21,7 +18,7 @@ def general_opt_history(topology):
     pso = GeneralOptimizerPSO(
         10, 2, {"c1": 0.5, "c2": 0.7, "w": 0.5}, topology=topology
     )
-    pso.optimize(sphere, 1000, verbose=0)
+    pso.optimize(sphere, 1000)
     return pso
 
 
@@ -42,7 +39,7 @@ def gbest_history():
     """Returns a GlobalBestPSO instance run for 1000 iterations for checking
     history"""
     pso = GlobalBestPSO(10, 2, {"c1": 0.5, "c2": 0.7, "w": 0.5})
-    pso.optimize(sphere, 1000, verbose=0)
+    pso.optimize(sphere, 1000)
     return pso
 
 
@@ -51,7 +48,7 @@ def gbest_reset():
     """Returns a GlobalBestPSO instance that has been run and reset to check
     default value"""
     pso = GlobalBestPSO(10, 2, {"c1": 0.5, "c2": 0.7, "w": 0.5})
-    pso.optimize(sphere, 10, verbose=0)
+    pso.optimize(sphere, 10)
     pso.reset()
     return pso
 
@@ -61,7 +58,7 @@ def lbest_history():
     """Returns a LocalBestPSO instance run for 1000 iterations for checking
     history"""
     pso = LocalBestPSO(10, 2, {"c1": 0.5, "c2": 0.7, "w": 0.5, "k": 2, "p": 2})
-    pso.optimize(sphere, 1000, verbose=0)
+    pso.optimize(sphere, 1000)
     return pso
 
 
@@ -70,7 +67,7 @@ def lbest_reset():
     """Returns a LocalBestPSO instance that has been run and reset to check
     default value"""
     pso = LocalBestPSO(10, 2, {"c1": 0.5, "c2": 0.7, "w": 0.5, "k": 2, "p": 2})
-    pso.optimize(sphere, 10, verbose=0)
+    pso.optimize(sphere, 10)
     pso.reset()
     return pso
 
@@ -80,7 +77,7 @@ def binary_history():
     """Returns a BinaryPSO instance run for 1000 iterations for checking
     history"""
     pso = BinaryPSO(10, 2, {"c1": 0.5, "c2": 0.7, "w": 0.5, "k": 2, "p": 2})
-    pso.optimize(sphere, 1000, verbose=0)
+    pso.optimize(sphere, 1000)
     return pso
 
 
@@ -89,7 +86,7 @@ def binary_reset():
     """Returns a BinaryPSO instance that has been run and reset to check
     default value"""
     pso = BinaryPSO(10, 2, {"c1": 0.5, "c2": 0.7, "w": 0.5, "k": 2, "p": 2})
-    pso.optimize(sphere, 10, verbose=0)
+    pso.optimize(sphere, 10)
     pso.reset()
     return pso
 
diff --git a/tests/optimizers/test_general_optimizer.py b/tests/optimizers/test_general_optimizer.py
index eac1d5b2..7936a114 100644
--- a/tests/optimizers/test_general_optimizer.py
+++ b/tests/optimizers/test_general_optimizer.py
@@ -1,13 +1,11 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
-# Import modules
-import pytest
 import numpy as np
+import pytest
 
-# Import from package
+from pyswarms.backend.topology import Random, Ring, VonNeumann
 from pyswarms.single import GeneralOptimizerPSO
-from pyswarms.backend.topology import Star, Ring, Pyramid, Random, VonNeumann
 from pyswarms.utils.functions.single_obj import sphere
 
 
@@ -236,5 +234,5 @@ def test_ftol_effect(options, topology):
     pso = GeneralOptimizerPSO(
         10, 2, options=options, topology=topology, ftol=1e-1
     )
-    pso.optimize(sphere, 2000, verbose=0)
+    pso.optimize(sphere, 2000)
     assert np.array(pso.cost_history).shape != (2000,)
diff --git a/tests/optimizers/test_global_best.py b/tests/optimizers/test_global_best.py
index ef10f1d0..5a2020a5 100644
--- a/tests/optimizers/test_global_best.py
+++ b/tests/optimizers/test_global_best.py
@@ -1,11 +1,9 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
-# Import modules
-import pytest
 import numpy as np
+import pytest
 
-# Import from package
 from pyswarms.single import GlobalBestPSO
 from pyswarms.utils.functions.single_obj import sphere
 
@@ -109,5 +107,5 @@ def test_training_history_shape(gbest_history, history, expected_shape):
 def test_ftol_effect(options):
     """Test if setting the ftol breaks the optimization process accodingly"""
     pso = GlobalBestPSO(10, 2, options=options, ftol=1e-1)
-    pso.optimize(sphere, 2000, verbose=0)
+    pso.optimize(sphere, 2000)
     assert np.array(pso.cost_history).shape != (2000,)
diff --git a/tests/optimizers/test_local_best.py b/tests/optimizers/test_local_best.py
index b85db283..1a991df4 100644
--- a/tests/optimizers/test_local_best.py
+++ b/tests/optimizers/test_local_best.py
@@ -1,11 +1,9 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
-# Import modules
-import pytest
 import numpy as np
+import pytest
 
-# Import from package
 from pyswarms.single import LocalBestPSO
 from pyswarms.utils.functions.single_obj import sphere
 
@@ -130,5 +128,5 @@ def test_training_history_shape(lbest_history, history, expected_shape):
 def test_ftol_effect(options):
     """Test if setting the ftol breaks the optimization process accodingly"""
     pso = LocalBestPSO(10, 2, options=options, ftol=1e-1)
-    pso.optimize(sphere, 2000, verbose=0)
+    pso.optimize(sphere, 2000)
     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 169fd198..96228765 100644
--- a/tests/optimizers/test_objective_func_with_kwargs.py
+++ b/tests/optimizers/test_objective_func_with_kwargs.py
@@ -1,11 +1,9 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
-# Import modules
 import pytest
 import numpy as np
 
-# Import from package
 from pyswarms.single import GlobalBestPSO, LocalBestPSO
 from pyswarms.utils.functions.single_obj import rosenbrock
 
@@ -31,9 +29,7 @@ def test_global_kwargs(func):
     )
 
     # 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, a=1, b=100)
 
     assert np.isclose(cost, 0, rtol=1e-03)
     assert np.isclose(pos[0], 1.0, rtol=1e-03)
@@ -56,7 +52,7 @@ def test_global_kwargs_without_named_arguments(func):
     )
 
     # run it
-    cost, pos = opt_ps.optimize(func, 1000, verbose=3, a=1, b=100)
+    cost, pos = opt_ps.optimize(func, 1000, a=1, b=100)
 
     assert np.isclose(cost, 0, rtol=1e-03)
     assert np.isclose(pos[0], 1.0, rtol=1e-03)
@@ -78,7 +74,7 @@ def test_global_no_kwargs(func):
     )
 
     # run it
-    cost, pos = opt_ps.optimize(func, 1000, print_step=10, verbose=3)
+    cost, pos = opt_ps.optimize(func, 1000)
 
     assert np.isclose(cost, 0, rtol=1e-03)
     assert np.isclose(pos[0], 1.0, rtol=1e-03)
@@ -100,9 +96,7 @@ def test_local_kwargs(func):
     )
 
     # 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, a=1, b=100)
 
     assert np.isclose(cost, 0, rtol=1e-03)
     assert np.isclose(pos[0], 1.0, rtol=1e-03)
@@ -124,7 +118,7 @@ def test_local_no_kwargs(func):
     )
 
     # run it
-    cost, pos = opt_ps.optimize(func, iters=1000, print_step=10, verbose=3)
+    cost, pos = opt_ps.optimize(func, iters=1000)
 
     assert np.isclose(cost, 0, rtol=1e-03)
     assert np.isclose(pos[0], 1.0, rtol=1e-03)
@@ -147,7 +141,7 @@ def test_global_uneeded_kwargs(func):
 
     # run it
     with pytest.raises(TypeError) as excinfo:
-        cost, pos = opt_ps.optimize(func, 1000, print_step=10, verbose=3, a=1)
+        cost, pos = opt_ps.optimize(func, 1000, a=1)
         assert "unexpected keyword" in str(excinfo.value)
 
 
@@ -167,7 +161,7 @@ def test_global_missed_kwargs(func):
 
     # run it
     with pytest.raises(TypeError) as excinfo:
-        cost, pos = opt_ps.optimize(func, 1000, print_step=10, verbose=3, a=1)
+        cost, pos = opt_ps.optimize(func, 1000, a=1)
         assert "missing 1 required positional argument" in str(excinfo.value)
 
 
@@ -187,7 +181,7 @@ def test_local_uneeded_kwargs(func):
 
     # run it
     with pytest.raises(TypeError) as excinfo:
-        cost, pos = opt_ps.optimize(func, 1000, print_step=10, verbose=3, a=1)
+        cost, pos = opt_ps.optimize(func, 1000, a=1)
         assert "unexpected keyword" in str(excinfo.value)
 
 
@@ -207,7 +201,7 @@ def test_local_missed_kwargs(func):
 
     # run it
     with pytest.raises(TypeError) as excinfo:
-        cost, pos = opt_ps.optimize(func, 1000, print_step=10, verbose=3, a=1)
+        cost, pos = opt_ps.optimize(func, 1000, a=1)
         assert "missing 1 required positional argument" in str(excinfo.value)
 
 
@@ -227,9 +221,7 @@ def test_local_wrong_kwargs(func):
 
     # run it
     with pytest.raises(TypeError) as excinfo:
-        cost, pos = opt_ps.optimize(
-            func, 1000, print_step=10, verbose=3, c=1, d=100
-        )
+        cost, pos = opt_ps.optimize(func, 1000, print_step=10, c=1, d=100)
         assert "unexpected keyword" in str(excinfo.value)
 
 
@@ -249,7 +241,5 @@ def test_global_wrong_kwargs(func):
 
     # run it
     with pytest.raises(TypeError) as excinfo:
-        cost, pos = opt_ps.optimize(
-            func, 1000, print_step=10, verbose=3, c=1, d=100
-        )
+        cost, pos = opt_ps.optimize(func, 1000, c=1, d=100)
         assert "unexpected keyword" in str(excinfo.value)