pyswarms.base package¶
The pyswarms.base
module implements base
swarm classes to implement variants of particle swarm optimization.
pyswarms.base module¶
Base class for singleobjective Particle Swarm Optimization implementations.
All methods here are abstract and raise a NotImplementedError
when not used. When defining your own swarm implementation,
create another class,
>>> class MySwarm(SwarmBase):
>>> def __init__(self):
>>> super(MySwarm, self).__init__()
and define all the necessary methods needed.
As a guide, check the global best and local best implementations in this package.
Note
Regarding options
, it is highly recommended to
include parameters used in position and velocity updates as
keyword arguments. For parameters that affect the topology of
the swarm, it may be much better to have them as positional
arguments.
See also
pyswarms.single.global_best
 globalbest PSO implementation
pyswarms.single.local_best
 localbest PSO implementation
pyswarms.single.general_optimizer
 a more general PSO implementation with a custom topology

class
pyswarms.base.base_single.
SwarmOptimizer
(n_particles, dimensions, options, bounds=None, velocity_clamp=None, center=1.0, ftol=inf, ftol_iter=1, init_pos=None)[source]¶ Bases:
abc.ABC

__init__
(n_particles, dimensions, options, bounds=None, velocity_clamp=None, center=1.0, ftol=inf, ftol_iter=1, init_pos=None)[source]¶ Initialize the swarm
Creates a Swarm class depending on the values initialized

options
¶ a dictionary containing the parameters for the specific optimization technique
 c1 : float
 cognitive parameter
 c2 : float
 social parameter
 w : float
 inertia parameter
Type: dict with keys {'c1', 'c2', 'w'}

bounds
¶ a tuple of size 2 where the first entry is the minimum bound while the second entry is the maximum bound. Each array must be of shape
(dimensions,)
.Type: tuple of numpy.ndarray, optional

velocity_clamp
¶ a tuple of size 2 where the first entry is the minimum velocity and the second entry is the maximum velocity. It sets the limits for velocity clamping.
Type: tuple, optional


_abc_impl
= <_abc_data object>¶

_populate_history
(hist)[source]¶ Populate all history lists
The
cost_history
,mean_pbest_history
, andneighborhood_best
is expected to have a shape of(iters,)
,on the other hand, thepos_history
andvelocity_history
are expected to have a shape of(iters, n_particles, dimensions)
Parameters: hist (collections.namedtuple) – Must be of the same type as self.ToHistory

optimize
(objective_func, iters, n_processes=None, **kwargs)[source]¶ Optimize the swarm for a number of iterations
Performs the optimization to evaluate the objective function
objective_func
for a number of iterationsiter.
Parameters: Raises: NotImplementedError
– When this method is not implemented.

reset
()[source]¶ Reset the attributes of the optimizer
All variables/atributes that will be reinitialized when this method is defined here. Note that this method can be called twice: (1) during initialization, and (2) when this is called from an instance.
It is good practice to keep the number of resettable attributes at a minimum. This is to prevent spamming the same object instance with various swarm definitions.
Normally, swarm definitions are as atomic as possible, where each type of swarm is contained in its own instance. Thus, the following attributes are the only ones recommended to be resettable:
 Swarm position matrix (self.pos)
 Velocity matrix (self.pos)
 Best scores and positions (gbest_cost, gbest_pos, etc.)
Otherwise, consider using positional arguments.

Base class for singleobjective discrete Particle Swarm Optimization implementations.
All methods here are abstract and raises a NotImplementedError
when not used. When defining your own swarm implementation,
create another class,
>>> class MySwarm(DiscreteSwarmOptimizer):
>>> def __init__(self):
>>> super(MySwarm, self).__init__()
and define all the necessary methods needed.
As a guide, check the discrete PSO implementations in this package.
Note
Regarding options
, it is highly recommended to
include parameters used in position and velocity updates as
keyword arguments. For parameters that affect the topology of
the swarm, it may be much better to have them as positional
arguments.
See also
pyswarms.discrete.binary
 binary PSO implementation

class
pyswarms.base.base_discrete.
DiscreteSwarmOptimizer
(n_particles, dimensions, binary, options, velocity_clamp=None, init_pos=None, ftol=inf, ftol_iter=1)[source]¶ Bases:
abc.ABC

__init__
(n_particles, dimensions, binary, options, velocity_clamp=None, init_pos=None, ftol=inf, ftol_iter=1)[source]¶ Initialize the swarm.
Creates a
numpy.ndarray
of positions depending on the number of particles needed and the number of dimensions. The initial positions of the particles depends on the argumentbinary
, which governs if a binary matrix will be produced.
binary
¶ a trigger to generate a binary matrix for the swarm’s initial positions. When passed with a
False
value, random integers from 0 todimensions
are generated.Type: boolean

options
¶ a dictionary containing the parameters for the specific optimization technique
 c1 : float
 cognitive parameter
 c2 : float
 social parameter
 w : float
 inertia parameter
Type: dict with keys {'c1', 'c2', 'w'}

velocity_clamp
¶ a tuple of size 2 where the first entry is the minimum velocity and the second entry is the maximum velocity. It sets the limits for velocity clamping.
Type: tuple, optional

ftol
¶ relative error in objective_func(best_pos) acceptable for convergence. Default is
np.inf
.Type: float, optional

ftol_iter
¶ number of iterations over which the relative error in objective_func(best_pos) is acceptable for convergence. Default is
1
Type: int

options
a dictionary containing the parameters for a specific optimization technique
Type: dict


_abc_impl
= <_abc_data object>¶

_populate_history
(hist)[source]¶ Populate all history lists
The
cost_history
,mean_pbest_history
, andneighborhood_best
is expected to have a shape of(iters,)
,on the other hand, thepos_history
andvelocity_history
are expected to have a shape of(iters, n_particles, dimensions)
Parameters: hist (collections.namedtuple) – Must be of the same type as self.ToHistory

optimize
(objective_func, iters, n_processes=None, **kwargs)[source]¶ Optimize the swarm for a number of iterations
Performs the optimization to evaluate the objective function
objective_func
for a number of iterationsiter.
Parameters: Raises: NotImplementedError
– When this method is not implemented.

reset
()[source]¶ Reset the attributes of the optimizer
All variables/atributes that will be reinitialized when this method is defined here. Note that this method can be called twice: (1) during initialization, and (2) when this is called from an instance.
It is good practice to keep the number of resettable attributes at a minimum. This is to prevent spamming the same object instance with various swarm definitions.
Normally, swarm definitions are as atomic as possible, where each type of swarm is contained in its own instance. Thus, the following attributes are the only ones recommended to be resettable:
 Swarm position matrix (self.pos)
 Velocity matrix (self.pos)
 Best scores and positions (gbest_cost, gbest_pos, etc.)
Otherwise, consider using positional arguments.
