macop.algorithms.multi

Multi-objetive classes algorithm

Functions

moEvaluator(solution, evaluator, weights)

Classes

MOEAD(mu, T, initalizer, evaluator, …[, …])	Multi-Ojective Evolutionary Algorithm with Scalar Decomposition
MOSubProblem(index, weights, initalizer, …)	Specific MO sub problem used into MOEAD

class macop.algorithms.multi.MOEAD(mu, T, initalizer, evaluator, operators, policy, validator, maximise=True, parent=None, verbose=True)

Multi-Ojective Evolutionary Algorithm with Scalar Decomposition

mu

{int} – number of sub problems

T

{[float]} – number of neightbors for each sub problem

nObjectives

{int} – number of objectives (based of number evaluator)

initalizer

{function} – basic function strategy to initialize solution

evaluator

{[function]} – list of basic function in order to obtained fitness (multiple objectives)

operators

{[Operator]} – list of operator to use when launching algorithm

policy

{Policy} – Policy class implementation strategy to select operators

validator

{function} – basic function to check if solution is valid or not under some constraints

maximise

{bool} – specify kind of optimisation problem

verbose

{bool} – verbose or not information about the algorithm

population

[{Solution}] – population of solution, one for each sub problem

pfPop

[{Solution}] – pareto front population

weights

[[{float}]] – random weights used for custom mu sub problems

callbacks

{[Callback]} – list of Callback class implementation to do some instructions every number of evaluations and load when initializing algorithm

end()

Display end message into run method

initRun()

Method which initialiazes or re-initializes the whole algorithm context specifically for MOEAD

progress()

Log progress and apply callbacks if necessary

run(evaluations)

Run the local search algorithm

Parameters

evaluations – {int} – number of Local search evaluations

Returns

{Solution} – best solution found

class macop.algorithms.multi.MOSubProblem(index, weights, initalizer, evaluator, operators, policy, validator, maximise=True, parent=None, verbose=True)

Specific MO sub problem used into MOEAD

index

{int} – sub problem index

weights

{[float]} – sub problems objectives weights

initalizer

{function} – basic function strategy to initialize solution

evaluator

{function} – basic function in order to obtained fitness (mono or multiple objectives)

operators

{[Operator]} – list of operator to use when launching algorithm

policy

{Policy} – Policy class implementation strategy to select operators

validator

{function} – basic function to check if solution is valid or not under some constraints

maximise

{bool} – specify kind of optimisation problem

verbose

{bool} – verbose or not information about the algorithm

currentSolution

{Solution} – current solution managed for current evaluation

bestSolution

{Solution} – best solution found so far during running algorithm

callbacks

{[Callback]} – list of Callback class implementation to do some instructions every number of evaluations and load when initializing algorithm

run(evaluations)

Run the local search algorithm

Parameters

evaluations – {int} – number of evaluations

Returns

{Solution} – best solution found