macop.algorithms.multi.MOEAD

Multi-Ojective Evolutionary Algorithm with Scalar Decomposition algorithm

Functions

moEvaluator(_solution, _evaluator, _weights)

Classes

MOEAD(_mu, _T, _initalizer, _evaluator, …)

Multi-Ojective Evolutionary Algorithm with Scalar Decomposition

class macop.algorithms.multi.MOEAD.MOEAD(_mu, _T, _initalizer, _evaluator, _operators, _policy, _validator, _maximise=True, _parent=None)[source]

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 optimization problem

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()[source]

Display end message into run method

initRun()[source]

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

progress()[source]

Log progress and apply callbacks if necessary

run(_evaluations)[source]

Run the local search algorithm

Parameters

_evaluations – {int} – number of Local search evaluations

Returns

{Solution} – best solution found