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

Haoxiang Jie, Yizhong Wu, Jianjun Zhao (), Jianwan Ding and Liangliang
Additional contact information
Haoxiang Jie: China Shipbuilding Industry Corporation
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

Journal of Global Optimization, 2017, vol. 67, issue 1, No 19, 399-423

Abstract: 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.

Keywords: Multi-objective optimization; Kriging metamodel; Particle swarm optimization; Black-box function (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10898-016-0428-2

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