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An efficient multi-objective PSO algorithm assisted by Kriging metamodel for expensive black-box problems

Author

Listed:
  • Haoxiang Jie

    (China Shipbuilding Industry Corporation
    Huazhong University of Science and Technology)

  • Yizhong Wu

    (Huazhong University of Science and Technology)

  • Jianjun Zhao

    (Huazhong University of Science and Technology)

  • Jianwan Ding

    (Huazhong University of Science and Technology)

  • Liangliang

    (Sun Yet-Sen University)

Abstract

The huge computational overhead is the main challenge in the application of community based optimization methods, such as multi-objective particle swarm optimization and multi-objective genetic algorithm, to deal with the multi-objective optimization involving costly simulations. This paper proposes a Kriging metamodel assisted multi-objective particle swarm optimization method to solve this kind of expensively black-box multi-objective optimization problems. On the basis of crowding distance based multi-objective particle swarm optimization algorithm, the new proposed method constructs Kriging metamodel for each expensive objective function adaptively, and then the non-dominated solutions of the metamodels are utilized to guide the update of particle population. To reduce the computational cost, the generalized expected improvements of each particle predicted by metamodels are presented to determine which particles need to perform actual function evaluations. The suggested method is tested on 12 benchmark functions and compared with the original crowding distance based multi-objective particle swarm optimization algorithm and non-dominated sorting genetic algorithm-II algorithm. The test results show that the application of Kriging metamodel improves the search ability and reduces the number of evaluations. Additionally, the new proposed method is applied to the optimal design of a cycloid gear pump and achieves desirable results.

Suggested Citation

  • Haoxiang Jie & Yizhong Wu & Jianjun Zhao & Jianwan Ding & Liangliang, 2017. "An efficient multi-objective PSO algorithm assisted by Kriging metamodel for expensive black-box problems," Journal of Global Optimization, Springer, vol. 67(1), pages 399-423, January.
    • Handle: RePEc:spr:jglopt:v:67:y:2017:i:1:d:10.1007_s10898-016-0428-2
    DOI: 10.1007/s10898-016-0428-2
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    Cited by:

    1. Yaohui Li & Jingfang Shen & Ziliang Cai & Yizhong Wu & Shuting Wang, 2021. "A Kriging-Assisted Multi-Objective Constrained Global Optimization Method for Expensive Black-Box Functions," Mathematics, MDPI, vol. 9(2), pages 1-20, January.
    2. Alkebsi, Khalil & Du, Wenli, 2021. "Surrogate-assisted multi-objective particle swarm optimization for the operation of CO2 capture using VPSA," Energy, Elsevier, vol. 224(C).

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