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Two-Stage Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization

Author

Listed:
  • Kai Zhang

    (Faculty of Humanities and Arts, Macau University of Science and Technology, Macao 999078, China
    These authors contributed equally to this work.)

  • Siyuan Zhao

    (Faculty of Humanities and Arts, Macau University of Science and Technology, Macao 999078, China
    These authors contributed equally to this work.)

  • Hui Zeng

    (School of Design, Jiangnan University, Wuxi 214122, China)

  • Junming Chen

    (Faculty of Humanities and Arts, Macau University of Science and Technology, Macao 999078, China)

Abstract

The core issue in handling constrained multi-objective optimization problems (CMOP) is how to maintain a balance between objectives and constraints. However, existing constrained multi-objective evolutionary algorithms (CMOEAs) often fail to achieve the desired performance when confronted with complex feasible regions. Building upon this theoretical foundation, a two-stage archive-based constrained multi-objective evolutionary algorithm (CMOEA-TA) based on genetic algorithms (GA) is proposed. In CMOEA-TA, First stage: The archive appropriately relaxes constraints based on the proportion of feasible solutions and constraint violations, compelling the population to explore more search space. Second stage: Sharing valuable information between the archive and the population, while embedding constraint dominance principles to enhance the feasibility of solutions. In addition an angle-based selection strategy was used to select more valuable solutions to increase the diversity of the population. To verify its effectiveness, CMOEA-TA was tested on 54 CMOPs in 4 benchmark suites and 7 state-of-the-art algorithms were compared. The experimental results show that it is far superior to seven competitors in inverse generation distance (IGD) and hypervolume (HV) metrics.

Suggested Citation

  • Kai Zhang & Siyuan Zhao & Hui Zeng & Junming Chen, 2025. "Two-Stage Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization," Mathematics, MDPI, vol. 13(3), pages 1-25, January.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:3:p:470-:d:1580936
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    References listed on IDEAS

    as
    1. Hao, Lupeng & Peng, Weihang & Liu, Junhua & Zhang, Wei & Li, Yuan & Qin, Kaixuan, 2025. "Competition-based two-stage evolutionary algorithm for constrained multi-objective optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 230(C), pages 207-226.
    2. Jin, Lingkang & Kazemi, Milad & Comodi, Gabriele & Papadimitriou, Christina, 2024. "Assessing battery degradation as a key performance indicator for multi-objective optimization of multi-carrier energy systems," Applied Energy, Elsevier, vol. 361(C).
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