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A novel multimodal multiobjective memetic algorithm with a local detection mechanism and a clustering-based selection strategy

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Abstract

In real-world scenes involving multimodal multiobjective optimization, there may exist different Pareto optimal sets (PSs) for the same Pareto front (PF), and some PFs even need to reserve all PSs, including local and global PSs. Most existing multimodal multiobjective optimization algorithms often distinguish solutions according to their diversity and convergence performances in the objective space. However, they pay little attention to the potential of solutions in the decision space. To solve this issue, a novel multimodal multiobjective memetic algorithm based on a local detection mechanism and a clustering-based selection strategy is proposed in this paper. To reserve more global and local PSs in the decision space, a density-based clustering method is adopted in the local detection mechanism, assisting in collecting solutions in the local clusters. Furthermore, in the clustering-based selection strategy, two different clustering methods are applied to different situations according to the ratio of the local optimal solutions. Extensive experimental results and performance comparisons with state-of-the-art algorithms show the superiority of our proposed algorithm.

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Data availability statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (NSFC) under Grants 61876110 and 61806130, the Joint Funds of the NSFC under Key Program Grant U1713212, the Shenzhen Scientific Research and Development Funding Program under Grant JCYJ20190808164211203, the Guangdong “Pearl River Talent Recruitment Program” under Grant 2019ZT08X603, and Shenzhen Science and Technology Innovation Commission (R2020A045).

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Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, People’s Republic of China

    Naili Luo, Yulong Ye, Wu Lin, Qiuzhen Lin & Victor C. M. Leung

  2. College of Optoelectronic Engineering, Shenzhen University, Shenzhen, People’s Republic of China

    Naili Luo

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  1. Naili Luo
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  2. Yulong Ye
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Correspondence to Wu Lin, Qiuzhen Lin or Victor C. M. Leung.

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Luo, N., Ye, Y., Lin, W. et al. A novel multimodal multiobjective memetic algorithm with a local detection mechanism and a clustering-based selection strategy. Memetic Comp. 15, 31–43 (2023). https://doi.org/10.1007/s12293-022-00353-0

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