Decomposition-based multi-objective evolutionary algorithm with customized evolution strategy according to population state

doi:10.1016/j.asoc.2025.113639

Decomposition-based multi-objective evolutionary algorithm with customized evolution strategy according to population state

2025-07-26

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DOI：10.1016/j.asoc.2025.113639

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Abstract

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• The population state is automatically identified as either converging or stagnating. • When the population is converging, a modified Tchebyche aggregation function is used to place greater emphasis on solution convergence. • When the population is stagnating, the diversity is emphasized by a set of improved operations, including mating, crossover, mutation, and periodical weight vector update. • Experimental results validate the performance of the proposed approach.

Keywords:

converging

stagnating

Tchebyche aggregation function

diversity

improved operations

Decomposition-based multi-objective evolutionary algorithm with customized evolution strategy according to population state

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