PySwallow

PySwallow is an extensible toolkit for PSO.

The library aims to provide a high-level declarative interface which
ensures that PSOs can be implemented and customised with ease. PySwallow
features an extensible framework which allows researchers to provide
custom implementations which interface with existing functionality.

License: MIT
Python Versions: 3.7+

Features:

High-level module for Particle Swarm Optimisation.
Extensible API for implementing new functionality.

Installation:

To install PySwallow, run this command in your terminal:

$ pip install pyswallow

Basic Usage:

PySwallow aims to provide a high-level interface for PSO - the code
below demonstrates just how easy running an optimisation procedure
can be.

import pyswallow as ps
from pyswallow.utils.functions import single_objective as fx
bounds = {
    'x0': [-1e6, 1e6],
    'x1': [-1e6, 1e6],
    'x2': [-1e6, 1e6]
}
optimiser = ps.Swarm(bounds=bounds, n_swallows=30, n_iterations=100)
optimiser.optimise(fx.sphere)

MPSwarm Example:

PySwallow can also be used in a multiprocessing case - using different
CPUs for each function evaluation. An example can be seen below:

import numpy as np
import pyswallow as ps
from pyswallow.mp.mp_swarm import MPSwarm
from pyswallow.swallows.so_swallow import Swallow
bounds = {
    'x0': [-1e6, 1e6],
    'x1': [-1e6, 1e6],
    'x2': [-1e6, 1e6]
}
def mp_sphere(swallow: Swallow) -> Swallow:
    swallow.fitness = np.sum(np.square(swallow.position))
    return swallow
optimiser = MPSwarm(
    bounds=bounds, 
    n_swallows=30, 
    n_iterations=100,
    cores=4
)
optimiser.optimise(mp_sphere)

History:

The optimisation history is written to a History data structure
to allow the user to further investigate the optimisation procedure
upon completion. This is a powerful tool, letting the user define custom
history classes which can record whichever data the user desires.

Tracking the history of the optimisation process allows for plotting
of the results, an example demonstration is seen in the
plot_fitness_history function - this can be further customised
through the designation of a PlotDesigner object which provides
formatting instructions for the graphing tools.

Constraints:

PySwallow allows the user to define a set of constraints for the
optimisation problem - this is achieved through inheriting a template
class and implementing the designated method. An example of which is
demonstrated below:

from pyswallow.constraints.base_constraints import PositionConstraint
class UserConstraint(PositionConstraint):
    def constrain(self, swallow):
        return swallow['x0'] > 0 and swallow['x1'] < 0
optimiser.constraint_manager.register_constraint(UserConstraint())

This provides the user with a large amount of freedom to define the
appropriate constraints and allows the ConstraintManager to deal with
the relevant constraints at the appropriate time.

Customisation:

Though the base Swarm is very effective, there may be aspects that the
user wishes to change, such as the boundary handler / inertia weight
methods. The library provides an extensible API which allows the user
to implement a variety of functions as well as develop their own with
templates provided in the form of Abstract Base Classes.

Attributes of the Swarm instance can be modified to alter how the
optimisation process will work, this is demonstrated below:

# altering the boundary handling method
from pyswallow.handlers.boundary_handler import NearestBH
optimiser.bh = NearestBH(lb, ub)

# altering the inertia weight handler
from pyswallow.handlers.inertia_handler import LinearIWH
optimiser.iwh = LinearIWH(w_init=0.7, w_end=0.4, n_iterations=100)

It is also possible to define alternative termination criteria through
implementation of a TerminationManager class, a couple of examples
are demonstrated below:

# using elapsed time as the termination criteria
from pyswallow.utils.termination_manager import TimeTerminationManager
optimiser.termination_manager = TimeTerminationManager(t_budget=10_000)

# using error as the termination criteria
from pyswallow.utils.termination_manager import ErrorTerminationManager
optimiser.termination_manager = ErrorTerminationManager(
    optimiser, target=0.0, threshold=1e-3
)