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Two-Stage Evolutionary Algorithm Using Clustering for Multimodal Multi-objective Optimization with Imbalance Convergence and Diversity

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Algorithms and Architectures for Parallel Processing (ICA3PP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 13157))

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Abstract

This paper proposes a two-stage multimodal multi-objective evolutionary algorithm using clustering, termed as TS_MMOEAC. In the first-stage evolution of TS_MMOEAC, the convergence-penalized density (CPD) based k-means clustering is used to divide population into multiple subpopulations. Subsequently, a local archive mechanism is adopted to maintain diverse local Pareto optimal solutions in decision space. In the second-stage evolution, an identical k-means clustering based on distance among individuals in decision space is applied to form multiple subpopulations. In this case, the convergence performance of local Pareto optimal solutions is accelerated. Meanwhile, equivalent Pareto optimal solutions with the imbalance between convergence and diversity are located by a similar local archive method with a larger clearing radius. Experimental results validate the superior performance of TS_MMOEAC, and the proposed TS_MMOEAC is capable of finding equivalent Pareto optimal solutions with the imbalance between convergence and diversity.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61873240).

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China

    Guoqing Li, Wanliang Wang & Yule Wang

Authors
  1. Guoqing Li
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  2. Wanliang Wang
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  3. Yule Wang
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Corresponding author

Correspondence to Wanliang Wang .

Editor information

Editors and Affiliations

  1. Xiamen University, Xiamen, China

    Yongxuan Lai

  2. Beijing Normal University, Zhuhai, China

    Tian Wang

  3. Xiamen University, Xiamen, China

    Min Jiang

  4. Tianjin University, Tianjin, China

    Guangquan Xu

  5. Hunan University, Changsha, China

    Wei Liang

  6. University of Naples Parthenope, Naples, Italy

    Aniello Castiglione

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Li, G., Wang, W., Wang, Y. (2022). Two-Stage Evolutionary Algorithm Using Clustering for Multimodal Multi-objective Optimization with Imbalance Convergence and Diversity. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13157. Springer, Cham. https://doi.org/10.1007/978-3-030-95391-1_36

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  • DOI: https://doi.org/10.1007/978-3-030-95391-1_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95390-4

  • Online ISBN: 978-3-030-95391-1

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