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A novel multi-population evolutionary algorithm based on hybrid collaboration for constrained multi-objective optimization
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AI
DOI：10.1016/j.swevo.2024.101581.png)
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Abstract
En 中文
Multi -population -based methods are widely employed for solving constrained multiobjective optimization problems (CMOPs). The population collaboration strategy is a critical part of multi -population algorithms, and different collaboration strategies perform well on different complex CMOPs. However, these single -population collaboration strategies are still challenging to adapt to various CMOPs with different characteristics. To address this issue, we propose a novel tri-population hybrid collaboration evolutionary algorithm called TPHCEA, which includes a constraint -relaxed population (denoted as mainpop ), a constraint -ignored auxiliary population (denoted as auxpop 1 ), and an auxiliary population (denoted as auxpop 2 ) for the original CMOP, to search optimal solutions in the feasible region. Specifically, due to the different complementarities of the two auxiliary populations, mainpop collaborates with auxpop 1 and auxpop 2 in a dynamic choice between strong and weak cooperation. The effectiveness of TP-HCEA is validated through comparative analysis with seven state-of-the-art algorithms in four CMOP benchmark suites and nine real -world problems.
Keywords:
Constrained multi-objective optimization
Multi-population algorithms
Hybrid collaboration
Constraint handling
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