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Population Feasibility State Guided Autonomous Constrained Multi-Objective Evolutionary Optimization

Author

Listed:
  • Mingcheng Zuo

    (Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, China)

  • Yuan Xue

    (Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

Many practical problems can be classified as constrained multi-objective optimization problems. Although various methods have been proposed for solving constrained multi-objective optimization problems, there is still a lack of research considering the integration of multiple constraint handling techniques. Given this, this paper combines the objective and constraint separation method with the multi-operator method, proposing a population feasibility state guided autonomous constrained evolutionary optimization method. This method first defines the feasibility state of the population based on both feasibility and ε feasibility of the solutions. Subsequently, a reinforcement learning model is employed to construct a mapping model between the population state and reproduction operators. Finally, based on the real-time population state, the mapping model is utilized to recommend the promising reproduction operator for the next generation. This approach demonstrates significant performance improvement for ε constrained mechanisms in constrained multi-objective optimization algorithms, and shows considerable advantages in comparison with state-of-the-art constrained multi-objective optimization algorithms.

Suggested Citation

  • Mingcheng Zuo & Yuan Xue, 2024. "Population Feasibility State Guided Autonomous Constrained Multi-Objective Evolutionary Optimization," Mathematics, MDPI, vol. 12(6), pages 1-24, March.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:6:p:913-:d:1360358
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    References listed on IDEAS

    as
    1. Morovati, Vahid & Pourkarimi, Latif, 2019. "Extension of Zoutendijk method for solving constrained multiobjective optimization problems," European Journal of Operational Research, Elsevier, vol. 273(1), pages 44-57.
    2. Hu, Hejuan & Sun, Xiaoyan & Zeng, Bo & Gong, Dunwei & Zhang, Yong, 2022. "Enhanced evolutionary multi-objective optimization-based dispatch of coal mine integrated energy system with flexible load," Applied Energy, Elsevier, vol. 307(C).
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