WU Tongxuan, JI Junzhong, YANG Cuicui. Research Progress and Challenges of Multimodal Multiobjective Evolutionary Algorithms for Global and Local Optimal Solutions[J]. Journal of Beijing University of Technology, 2025, 51(7): 867-882. DOI: 10.11936/bjutxb2024070023
    Citation: WU Tongxuan, JI Junzhong, YANG Cuicui. Research Progress and Challenges of Multimodal Multiobjective Evolutionary Algorithms for Global and Local Optimal Solutions[J]. Journal of Beijing University of Technology, 2025, 51(7): 867-882. DOI: 10.11936/bjutxb2024070023

    Research Progress and Challenges of Multimodal Multiobjective Evolutionary Algorithms for Global and Local Optimal Solutions

    • To reveal the recent research advances of multimodal multiobjective evolutionary algorithms for global and local optimal solutions, the multimodal multiobjective optimization problem (MMOP) with global and local optimal solution sets was first introduced and its related definitions and characteristics were provided. Second, a systematic category was presented according to the existing algorithms, and the technical characteristics of the methods were analyzed. Third, the test function sets with global and local optimal solution sets were introduced, and the common evaluation indicators were discussed. Finally, by analyzing the challenges, the future research directions of this field were identified.
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