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MOEADTensor

Module: opytimizer.optimizers.multi_objective.evolutionary.moead

Tensorized implementation of MOEA/D based on the following work: Z. Liang, H. Li, N. Yu, K. Sun and R. Cheng, "Bridging Evolutionary Multiobjective Optimization and GPU Acceleration via Tensorization," in IEEE Transactions on Evolutionary Computation, vol. 30, no. 1, pp. 420-434, Feb. 2026, doi: 10.1109/TEVC.2025.3555605.

Constructor​

MOEADTensor(params: Optional[Dict[str, Any]] = None, crossover_operator=None, mutation_operator=None, weight_vectors=None, decomposition_method: opytimizer.math.aggregation._BaseAggregation = None, neighborhood_size: int = None) -> None

Parameters​

ParameterTypeDefaultDescription
paramsOptional[Dict[str, Any]]None—
crossover_operatorNone—
mutation_operatorNone—
weight_vectorsNone—
decomposition_methodopytimizer.math.aggregation._BaseAggregationNone—
neighborhood_sizeintNone—

Methods​

compile​

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

Compiles additional information that is used by this optimizer.

Parameters​

ParameterTypeDefaultDescription
spaceopytimizer.core.space._MultiObjectiveTensorSpace—
kwargs—

evaluate​

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

Evaluates the search space according to the objective function.

Parameters​

ParameterTypeDefaultDescription
spaceopytimizer.core.space._MultiObjectiveTensorSpaceA Space object that will be evaluated.
functionopytimizer.core.function.FunctionA Function object serving as an objective function.

update​

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

Updates the agents' position array.

As each child has a different procedure of update, you will need to implement it directly on its class.

Parameters​

ParameterTypeDefaultDescription
spaceopytimizer.core.space._MultiObjectiveTensorSpace—
functionopytimizer.core.function.Function—