A dynamic dual-population differential evolution algorithm for constrained multi-objective optimization

doi:10.1016/j.measurement.2026.121588

A dynamic dual-population differential evolution algorithm for constrained multi-objective optimization

2026-04-22

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孙亮亮 (Liangliang Sun) *

DOI：10.1016/j.measurement.2026.121588

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Abstract

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• New technique uses infeasible solutions to guide search toward feasible regions. • Dynamic population size adjustment allocates computational resources efficiently. • Parameter self-adaptive strategy balances convergence and diversity. • Outperforms eight state-of-art CMOEAs on 48 benchmark and real-world cases.

Keywords:

constrained multi-objective optimization

differential evolution

dynamic population size

parameter self-adaptation

infeasible solution guidance

A dynamic dual-population differential evolution algorithm for constrained multi-objective optimization

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