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A dynamic dual-population co-evolutionary algorithm for constrained multi-objective optimization problems
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DOI：10.1007/s10586-026-06467-1.png)
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Abstract
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Multi-objective evolutionary algorithms have been widely used to solve constrained multi-objective optimization problems, but they still face many challenges in balancing the feasibility, convergence and diversity of the populations simultaneously. To address the above problems, this paper proposes a dynamic two-population co-evolutionary algorithm (CHEA), which balances feasibility, convergence and diversity at different stages by dynamically adjusting the number of offspring of the two populations. The algorithm focuses on diversity in the early stage and convergence and feasibility in the later stage, and introduces a hierarchical guided mutation strategy (HGMS) to improve convergence under constraints, and adopts the neighborhood distribution distance (NDD) to maintain diversity more effectively. The superior performance of CHEA in solving constrained multi-objective optimization problems is verified through comparative experiments on 37 benchmark test problems and three real problems.
Keywords:
Dynamic dual-population
Constrained multi-objective optimization
Hierarchical guided mutation strategy
Coevolutionary algorithm
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