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A competition-driven two-phase evolutionary algorithm for constrained multi-objective optimization
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DOI：10.1016/j.swevo.2026.102322.png)
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Abstract
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• A competition-driven two-stage evolutionary algorithm (AAPEA) for constrained multi-objective optimization. • Dual-population co-evolution enhances diversity and search guidance. • Adaptive size adjustment reduces auxiliary population cost. • CSO based competitive search operators promote convergence to high-quality solutions.
Keywords:
constrained multi-objective optimization
two-stage evolutionary algorithm
dual-population co-evolution
adaptive size adjustment
competitive search operators
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