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A neighborhood-assisted evolutionary algorithm for multimodal multi-objective optimization

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Abstract

Multi-modal multi-objective optimization problems (MMOPs) involve multiple Pareto sets (PSs) in decision space corresponding to the same Pareto front (PF) in objective space. The difficulty lies in locating multiple equivalent PSs while ensuring a well-converged and well-distributed PF. To address this, a neighborhood-assisted reproduction strategy is proposed. Through interactions with non-dominated solutions, the generated offspring could spread out along the PF, while ineractions with neighbors could improve the convergence ability. Importantly, individuals can participate in multiple neighborhoods, reducing the computational burden. Additionally, a neighborhood-assisted environmental selection strategy is prposed to encourage exploration of diverse solution regions, ensuring a balanced distribution of the population and preservation of multiple PSs. Comparative experiments are implemented on the CEC 2019 MMOPs test suite, and the superior performance of the proposed algorithm is demonstrated in comparison to several state-of-the-art approaches.

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Acknowledgements

This work was supported in part by the Natural Science Foundation of Henan Province of China under Grant 242300421413, in part by the Key Research Projects of Higher Education Institutions in Henan Province under Grant 24B520039, in part by Henan Province Science and Technology Research Projects under Grant 242102110334 and 242102110375, in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LQ23F030007, and the Fundamental Research Funds for the Provincial Universities of Zhejiang under Grant SJLY2023010.

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Authors and Affiliations

  1. School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, 450000, China

    Weiwei Zhang, Jiaqiang Li & Weizheng Zhang

  2. Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, China

    Guoqing Li

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  1. Weiwei Zhang
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Contributions

Weiwei Zhang: Conceptualization, Methodology, Software, Writing - review & editing. Jiaqiang Li: Data curation, Supervision, Funding acquisition. Guoqing Li: Methodology, Writing - review & editing. Weizheng Zhang: Validation.

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Correspondence to Guoqing Li.

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Zhang, W., Li, J., Li, G. et al. A neighborhood-assisted evolutionary algorithm for multimodal multi-objective optimization. Memetic Comp. (2024). https://doi.org/10.1007/s12293-024-00410-w

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