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A clustering differential evolution algorithm with neighborhood-based dual mutation operator for multimodal multiobjective optimization
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AI
DOI：10.1016/j.eswa.2022.119438.png)
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Abstract
En 中文
Multimodal multiobjective optimization problems (MMOPs) possess multiple equivalent Pareto optimal sets (PSs) corresponding to the same Pareto front (PF). Numerous multimodal multiobjective evolutionary al-gorithms (MMEAs) have been developed to solve MMOPs. However, how to locate multiple high quality equivalent PSs and obtain a uniformly distributed PF simultaneously remains a challenging task. This paper explores a novel multimodal multiobjective differential evolution algorithm, termed MMOcDE, for solving MMOPs. In MMOcDE, a neighborhood-based dual mutation strategy is proposed by developing two novel mutation operators. The two novel mutation operators provide a better tradeoff between exploration and exploitation in locating multiple equivalent PSs. In addition, a clustering-based environmental selection mechanism is designed by integrating an affinity propagation method. The affinity propagation method improves the distribution of solutions in decision and objective spaces, and a harmonic averaged distance is adopted to promote the diversity of a population. The proposed MMO_CDE is compared against state-of-the-art MMEAs on a test suite of CEC 2019 benchmark functions and a map-based practical application. Empirical results validate the superiority of the proposed MMOcDE regarding locating multiple equivalent PSs and obtaining a well-distributed PF.
Keywords:
Differential evolution
Multimodal multiobjective optimization
problems
Multimodal multiobjective evolutionary
algorithms
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W