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Deep reinforcement learning-based two-stage coevolutionary framework for multimodal multi-objective optimization
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DOI：10.1016/j.asoc.2026.114836.png)
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Abstract
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• Deep reinforcement learning based adaptive evolutionary operator selection strategy. • Incorporating adaptive operators into a two-stage co-evolutionary framework proposing MMOEA-DRL. • MMOEA-DRL achieves 64.71% optimality and outperforms other methods by more than 55.74% in the decision space.
Keywords:
Deep reinforcement learning
Adaptive evolutionary operators
Two-stage co-evolutionary framework
Multimodal multi-objective optimization
Evolutionary algorithms
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6.6
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