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A Many-Objective Evolutionary Algorithm Based on Indicator and Decomposition

Author

Listed:
  • Yizhang Xia

    (School of Cyberspace Science, Xiangtan University, Xiangtan 411105, China)

  • Jianzun Huang

    (School of Cyberspace Science, Xiangtan University, Xiangtan 411105, China)

  • Xijun Li

    (School of Cyberspace Science, Xiangtan University, Xiangtan 411105, China)

  • Yuan Liu

    (School of Cyberspace Science, Xiangtan University, Xiangtan 411105, China)

  • Jinhua Zheng

    (School of Cyberspace Science, Xiangtan University, Xiangtan 411105, China)

  • Juan Zou

    (School of Cyberspace Science, Xiangtan University, Xiangtan 411105, China)

Abstract

In the field of many-objective evolutionary optimization algorithms (MaOEAs), how to maintain the balance between convergence and diversity has been a significant research problem. With the increase of the number of objectives, the number of mutually nondominated solutions increases rapidly, and multi-objective evolutionary optimization algorithms, based on Pareto-dominated relations, become invalid because of the loss of selection pressure in environmental selection. In order to solve this problem, indicator-based many-objective evolutionary algorithms have been proposed; however, they are not good enough at maintaining diversity. Decomposition-based methods have achieved promising performance in keeping diversity. In this paper, we propose a MaOEA based on indicator and decomposition (IDEA) to keep the convergence and diversity simultaneously. Moreover, decomposition-based algorithms do not work well on irregular PFs. To tackle this problem, this paper develops a reference-points adjustment method based on the learning population. Experimental studies of several well-known benchmark problems show that IDEA is very effective compared to ten state-of-the-art many-objective algorithms.

Suggested Citation

  • Yizhang Xia & Jianzun Huang & Xijun Li & Yuan Liu & Jinhua Zheng & Juan Zou, 2023. "A Many-Objective Evolutionary Algorithm Based on Indicator and Decomposition," Mathematics, MDPI, vol. 11(2), pages 1-27, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:2:p:413-:d:1034095
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    References listed on IDEAS

    as
    1. Ioannis Giagkiozis & Robin C. Purshouse & Peter J. Fleming, 2015. "An overview of population-based algorithms for multi-objective optimisation," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(9), pages 1572-1599, July.
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