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Maximum angle evolutionary selection for many-objective optimization algorithm with adaptive reference vector

Author

Listed:
  • Zhijian Xiong

    (Yanshan University
    Tangshan University)

  • Jingming Yang

    (Yanshan University)

  • Zhiwei Zhao

    (Tangshan University)

  • Yongqiang Wang

    (Tangshan University)

  • Zhigang Yang

    (Yanshan University
    North China University of Science and Technology)

Abstract

How to maintain a good balance between convergence and diversity is particularly important for the performance of the many-objective evolutionary algorithms. Especially, the many-objective optimization problem is a complicated Pareto front, the many-objective evolutionary algorithm can easily converge to a narrow of the Pareto front. An efficient environmental selection and normalization method are proposed to address this issue. The maximum angle selection method based on vector angle is used to enhance the diversity of the population. The maximum angle rule selects the solution as reference vector can work well on complicated Pareto front. A penalty-based adaptive vector distribution selection criterion is adopted to balance convergence and diversity of the solutions. As the evolution process progresses, the new normalization method dynamically adjusts the implementation of the normalization. The experimental results show that new algorithm obtains 30 best results out of 80 test problems compared with other five many-objective evolutionary algorithms. A large number of experiments show that the proposed algorithm has better performance, when dealing with numerous many-objective optimization problems with regular and irregular Pareto Fronts.

Suggested Citation

  • Zhijian Xiong & Jingming Yang & Zhiwei Zhao & Yongqiang Wang & Zhigang Yang, 2023. "Maximum angle evolutionary selection for many-objective optimization algorithm with adaptive reference vector," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 961-984, March.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:3:d:10.1007_s10845-021-01865-1
    DOI: 10.1007/s10845-021-01865-1
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    References listed on IDEAS

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    1. Jaeseok Huh & Moon-jung Chae & Jonghun Park & Kwanho Kim, 2019. "A case-based reasoning approach to fast optimization of travel routes for large-scale AS/RSs," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1765-1778, April.
    2. Hao Liu & Yue Wang & Liangping Tu & Guiyan Ding & Yuhan Hu, 2019. "A modified particle swarm optimization for large-scale numerical optimizations and engineering design problems," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2407-2433, August.
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