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A decision variable classification strategy based on the degree of environmental change for dynamic multiobjective optimization

Author

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  • Sun, Hao
  • Wang, Cong
  • Li, Xiaxia
  • Hu, Ziyu

Abstract

Dynamic multiobjective optimization problems (DMOPs) are constantly changing over time, which requires algorithms to keep track of the location of the Pareto optimal front (POF) at different moments in time. In this work, a decision variable classification strategy based on the degree of environmental change (DVCEC) is proposed. To accurately capture the occurrence of environmental changes, DVCEC designs an adaptive change detection method based on multiple regions. Since environmental changes affect each decision variable to different degrees, DVCEC classifies decision variables into several types and applies an appropriate prediction method to each type. In addition, an adjustment strategy is developed to minimize the impact of inaccurate predictions. The proposed DVCEC is evaluated on 22 benchmark problems and compared with four algorithms. Statistical results show that DVCEC can quickly approach POF and uniformly distribute it in most problems.

Suggested Citation

  • Sun, Hao & Wang, Cong & Li, Xiaxia & Hu, Ziyu, 2024. "A decision variable classification strategy based on the degree of environmental change for dynamic multiobjective optimization," European Journal of Operational Research, Elsevier, vol. 313(1), pages 296-311.
    • Handle: RePEc:eee:ejores:v:313:y:2024:i:1:p:296-311
    DOI: 10.1016/j.ejor.2023.08.023
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    References listed on IDEAS

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    1. Fontes, Dalila B.M.M. & Homayouni, S. Mahdi & Gonçalves, José F., 2023. "A hybrid particle swarm optimization and simulated annealing algorithm for the job shop scheduling problem with transport resources," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1140-1157.
    2. Ma, Xuemin & Yang, Jingming & Sun, Hao & Hu, Ziyu & Wei, Lixin, 2021. "Feature information prediction algorithm for dynamic multi-objective optimization problems," European Journal of Operational Research, Elsevier, vol. 295(3), pages 965-981.
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