pyswarm¶

pso

Package Contents¶

Classes¶

Functions¶

_is_feasible(theList)¶

_format_fx_fs(objectives_pop, constraints_pop)¶

class MyMapEvaluator(evaluation_function, callback_on_evaluation)[source]¶

evaluate_all(x)[source]¶

class MyMultiprocessEvaluator(evaluation_function, callback_on_evaluation, numberOfCores)[source]¶

evaluate_all(x)[source]¶

pso(lb, ub, initialVectorGuess, theEvaluator, maxtime, callback_generation=lambda objectives, constraints: None, swarmsize=100, omega=0.5, phip=0.5, phig=0.5)[source]¶

Perform a particle swarm optimization (PSO)

lb: list: Lower bounds of each parameter
ub: list: upper bounds of each parameter
initialVectorGuess: list: initial vector guess for the solution (to be included inside population)

theEvaluator : object define before maxtime : float

The maximum time (in s) before stopping the algorithm

callback_generation: function lambda (bjectives (as list), constraints (as list)) per step: Useful to log convergence
swarmsize : int: The number of particles in the swarm (Default: 100)
omega : scalar: Particle velocity scaling factor (Default: 0.5)
phip : scalar: Scaling factor to search away from the particle’s best known position (Default: 0.5)
phig : scalar: Scaling factor to search away from the swarm’s best known position (Default: 0.5)

g : array: The swarm’s best known position (optimal design)
f : scalar: The objective value at g