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Multi-tasking evolutionary learning for constrained multi-modal multi-objective optimization
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AI
DOI：10.1016/j.asoc.2025.114116.png)
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Abstract
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• Integration of evolutionary multi-task optimization strategies into constrained multi-objective optimization. By transitioning between different strategies in two stages, the algorithm effectively balances convergence and diversity by controlling information exchange between different tasks. • We designed a new information transfer rate prediction function, through which the algorithm can effectively and reasonably control the information exchange between different tasks. • Extensive experimentation on multiple test sets, along with strategy validation experiments, demonstrates that the proposed algorithm is competitive in solving constrained multi-objective problems.
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