pyswarm

• pso

Package Contents

Classes

Functions

_is_feasible(theList)

_format_fx_fs(objectives_pop, constraints_pop)

class MyMapEvaluator(evaluation_function, callback_on_evaluation)

evaluate_all(x)

class MyMultiprocessEvaluator(evaluation_function, callback_on_evaluation, numberOfCores)

evaluate_all(x)

class MyArrayEvaluator(evaluation_function)

evaluate_all(list_of_x)

pso(lb, ub, initialVectorGuess, theEvaluator, terminationCondition, callback_generation=lambda objectives, constraints: None, swarmsize=100, omega=0.5, phip=0.5, phig=0.5)

Perform a particle swarm optimization (PSO)

Parameters:

• 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
• terminationCondition – Termination condition with shouldTerminated method
• callback_generation – function lambda (objectives (as list), constraints (as list)) per step
• swarmsize – Int. The number of particles in the swarm (Default: 100)
• omega – Float. Particle velocity scaling factor (Default: 0.5)
• phip – Float. Scaling factor to search away from the particle’s best known position (Default: 0.5)
• phig – Float. Scaling factor to search away from the swarm’s best known position (Default: 0.5)

Returns:

7. array The swarm’s best known position (optimal design). (f) scalar, the objective value at (g)