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MOEAD_DE

Module: opytimizer.optimizers.multi_objective.evolutionary.moead

MOEA/D-DE class, inherited from Optimizer.

References: Li, H., & Zhang, Q. (2008). Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE transactions on evolutionary computation, 13(2), 284-302.

Constructor​

MOEAD_DE(params: Optional[Dict[str, Any]] = None, CR: Union[float, int] = 1.0, nr: int = 2, F: Union[float, int] = 0.5, neighborhood_prob: float = 0.9, mutation_operator=None, weight_vectors=None, decomposition_function: opytimizer.math.aggregation._BaseAggregation = None) -> None

Parameters​

ParameterTypeDefaultDescription
paramsOptional[Dict[str, Any]]None—
CRUnion[float, int]1.0—
nrint2—
FUnion[float, int]0.5—
neighborhood_probfloat0.9—
mutation_operatorNone—
weight_vectorsNone—
decomposition_functionopytimizer.math.aggregation._BaseAggregationNone—

Methods​

compile​

compile(self, space: opytimizer.core.space._MultiObjectiveSpace, **kwargs) -> None

Compiles additional information that is used by this optimizer.

Parameters​

ParameterTypeDefaultDescription
spaceopytimizer.core.space._MultiObjectiveSpaceA Space object containing meta-information.
kwargs—

evaluate​

evaluate(self, space: opytimizer.core.space._MultiObjectiveSpace, function: opytimizer.core.function.Function) -> None

Evaluates the fitness of the agents.

Parameters​

ParameterTypeDefaultDescription
spaceopytimizer.core.space._MultiObjectiveSpaceSpace containing agents and evaluation-related information.
functionopytimizer.core.function.FunctionFunction to evaluate the fitness of the agents.

update​

update(self, space: opytimizer.core.space._MultiObjectiveSpace, function: opytimizer.core.function.Function) -> None

Updates the population using MOEA/D.

Parameters​

ParameterTypeDefaultDescription
spaceopytimizer.core.space._MultiObjectiveSpaceSpace containing agents and update-related information.
functionopytimizer.core.function.FunctionFunction to evaluate the fitness of the agents.