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A Novel Genetic Algorithm for Constrained Multimodal Multi-Objective Optimization Problems

Author

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  • Da Feng

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University, Shenyang 110819, China)

  • Jianchang Liu

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University, Shenyang 110819, China)

Abstract

This paper proposes a multitasking-based genetic algorithm (MTGA-CMMO) to solve constrained multimodal multi-objective optimization problems (CMMOPs). In MTGA-CMMO, the main task is assisted by two auxiliary tasks to obtain all the feasible Pareto solution sets. The constraint boundaries of auxiliary task 1 are dynamically adjusted, facilitating the main task’s population in crossing infeasible regions early in the evolution and providing more evolutionary direction later in the evolution. Auxiliary task 2 can contribute to the exploitation ability of the main task. Meanwhile, a probability-based leader mating selection mechanism is devised to improve the global search capability of MTGA-CMMO. Additionally, three environmental selection strategies are designed to correspond to the different tasks in MTGA-CMMO. Extensive experimental verification demonstrates that MTGA-CMMO outperforms other comparative algorithms across multiple test instances and one practical application problem.

Suggested Citation

  • Da Feng & Jianchang Liu, 2025. "A Novel Genetic Algorithm for Constrained Multimodal Multi-Objective Optimization Problems," Mathematics, MDPI, vol. 13(11), pages 1-33, June.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:11:p:1851-:d:1670320
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    References listed on IDEAS

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    1. Deb, Kalyanmoy & Tiwari, Santosh, 2008. "Omni-optimizer: A generic evolutionary algorithm for single and multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1062-1087, March.
    2. Zou, Dexuan & Gong, Dunwei & Ouyang, Haibin, 2023. "The dynamic economic emission dispatch of the combined heat and power system integrated with a wind farm and a photovoltaic plant," Applied Energy, Elsevier, vol. 351(C).
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