optiAlgorithms

Subpackages

• convergence
  • evolutionaryConvergence
  • hypervolume
  • interfaceConvergence
• pyswarm
  • pso

• NLOpt_Algorithm
• algorithmInterface
• monobjective_PSO
• multiObjective_GA

Package Contents

Classes

class MultiObjective_GA(theGenerator=MyGenerator)

Bases: optimeed.optimize.optiAlgorithms.algorithmInterface.AlgorithmInterface, optimeed.core.Option_class

Based on Platypus Library. Workflow: Define what to optimize and which function to call with a Problem Define the initial population with a Generator Define the algorithm. As options, define how to evaluate the elements with a Evaluator, i.e., for multiprocessing. Define what is the termination condition of the algorithm with TerminationCondition. Here, termination condition is a maximum time.

initialize(initialVectorGuess, listOfOptimizationVariables)

This function is called just before running optimization algorithm.

compute()

Launch the optimization

Returns:vector of optimal variables

set_evaluationFunction(evaluationFunction, callback_on_evaluation, numberOfObjectives, numberOfConstraints, array_evaluator)

Set the evaluation function and all the necessary callbacks

Parameters:

• evaluationFunction – check evaluateObjectiveAndConstraints()
• callback_on_evaluation – check callback_on_evaluation(). Call this function after performing the evaluation of the individuals
• numberOfObjectives – int, number of objectives
• numberOfConstraints – int, number of constraints
• array_evaluator – If True, evaluate each generation at once using numpy array. Use it only with care, as it dismisses some features (expert mode)

set_maxtime(maxTime)

Set maximum optimization time (in seconds)

__str__()

Return str(self).

get_convergence()

This function is called just before compute. Because the convergence is contained in opti algorithm, it must be created now.

add_terminationCondition(theTerminationCondition)

reset()

class Monobjective_PSO

Bases: optimeed.optimize.optiAlgorithms.algorithmInterface.AlgorithmInterface, optimeed.core.Option_class

Interface for the optimization algorithm

initialize(initialVectorGuess, listOfOptimizationVariables)

This function is called once parameters can’t be changed anymore, before “get_convergence”.

Parameters:

• initialVectorGuess – list of variables that describe the initial individual
• listOfOptimizationVariables – list of optimeed.optimize.optiVariable.OptimizationVariable

Returns:

compute()

Launch the optimization

Returns:vector of optimal variables

set_evaluationFunction(evaluationFunction, callback_on_evaluate, numberOfObjectives, _numberOfConstraints, array_evaluator)

Set the evaluation function and all the necessary callbacks

Parameters:

• evaluationFunction – check evaluateObjectiveAndConstraints()
• callback_on_evaluation – check callback_on_evaluation(). Call this function after performing the evaluation of the individuals
• numberOfObjectives – int, number of objectives
• numberOfConstraints – int, number of constraints
• array_evaluator – If True, evaluate each generation at once using numpy array. Use it only with care, as it dismisses some features (expert mode)

set_terminationCondition(theTerminationCondition)

set_maxtime(maxTime)

Set maximum optimization time (in seconds)

__str__()

Return str(self).

get_convergence()

Get the convergence of the optimization

Returns:InterfaceConvergence