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A Many-Objective Practical Swarm Optimization Based on Mixture Uniform Design and Game Mechanism

Author

Listed:
  • Chen Yan

    (South China Agriculture University, China)

  • Cai Mengxiang

    (South China Agriculture University, China)

  • Zheng Mingyong

    (South China Agriculture University, China)

  • Li Kangshun

    (Guangdong University of Science and Technology, China)

Abstract

In recent years, multi-objective optimization algorithms, especially many-objective optimization algorithms, have developed rapidly and effectively.Among them, the algorithm based on particle swarm optimization has the characteristics of simple principle, few parameters and easy implementation. However, these algorithms still have some shortcomings, but also face the problems of falling into the local optimal solution, slow convergence speed and so on. In order to solve these problems, this paper proposes an algorithm called MUD-GMOPSO, A Many-Objective Practical Swarm Optimization based on Mixture Uniform Design and Game mechanism. In this paper, the two improved methods are combined, and the convergence speed, accuracy and robustness of the algorithm are greatly improved. In addition, the experimental results show that the algorithm has better performance than the four latest multi-objective or high-dimensional multi-objective optimization algorithms on three widely used benchmarks: DTLZ, WFG and MAF.

Suggested Citation

  • Chen Yan & Cai Mengxiang & Zheng Mingyong & Li Kangshun, 2022. "A Many-Objective Practical Swarm Optimization Based on Mixture Uniform Design and Game Mechanism," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 16(1), pages 1-17, January.
    • Handle: RePEc:igg:jcini0:v:16:y:2022:i:1:p:1-17
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    References listed on IDEAS

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    1. Kangshun Li & Zhuozhi Liang & Shuling Yang & Zhangxing Chen & Hui Wang & Zhiyi Lin, 2019. "Performance Analyses of Differential Evolution Algorithm Based on Dynamic Fitness Landscape," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 13(1), pages 36-61, January.
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