Complementary surrogate-assisted differential evolution algorithm for expensive multi-objective problems under a limited computational budget

Cai，Xiwen1; Ruan，Gan2; Yuan，Bo3; Gao，Liang4

2023-06-01

10.1016/j.ins.2023.03.005

INFORMATION SCIENCES   影响因子和分区

0020-0255

1872-6291

Different surrogate-assisted strategies can greatly influence the optimization efficiency of surrogate-assisted multi-objective evolutionary algorithms. By hybridizing two complementary surrogate-assisted strategies, this study proposed an efficient surrogate-assisted differential evolution algorithm to optimize expensive multi-objective problems under a limited computational budget. The two proposed surrogate-assisted strategies balance global and local search for multi-objective optimization. Specifically, one strategy is an improved surrogate-based multi-objective local search method that is based on maximin angle-distance sequential sampling. Compared with the previous local search method that is based on Euclidian distance-based sampling, the improved local search method is more efficient because it can mitigate the scale difference of different objectives. The other surrogate-assisted strategy is prescreening based on a diversity-enhanced expected improvement matrix infill criterion. The proposed infill criterion aims to improve the diversity of approximate Pareto optimal solutions by considering distribution of candidate individuals in the objective space in the infill function. Within a limited computational burden, the performance of the proposed algorithm is demonstrated on a large set of multi-objective benchmark problems, as well as a real-world airfoil design problem. Experimental results show that the proposed algorithm performs significantly better than some existing algorithms on most problems investigated in this study.

Differential evolution Expensive multi-objective problems Infill criterion Maximin angle-distance sampling Surrogate-based local search

SCI ; EI

英语

通讯

National Natural Science Foundation of China["51805180","61976111","62250710682"]

Computer Science

Computer Science, Information Systems

WOS:000952067600001

ELSEVIER SCIENCE INC

20231213745731

Benchmarking ; Budget control ; Computational efficiency ; Evolutionary algorithms ; Infill drilling ; Local search (optimization) ; Pareto principle

Oil Field Production Operations:511.1 ; Optimization Techniques:921.5

COMPUTER SCIENCE

2-s2.0-85150052677

Scopus

1.Mechanical and Electrical Engineering College,Guangzhou University,Guangzhou,510006,China
2.School of Computer Science,University of Birmingham,Edgbaston Birmingham,B15 2TT,United Kingdom
3.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
4.The State Key Laboratory of Digital Manufacturing Equipment and Technology,School of Mechanical Science and Engineering,Huazhong University of Science and Technology,Wuhan,430074,China

Cai，Xiwen,Ruan，Gan,Yuan，Bo,et al. Complementary surrogate-assisted differential evolution algorithm for expensive multi-objective problems under a limited computational budget[J]. INFORMATION SCIENCES,2023,632:791-814.

Cai，Xiwen,Ruan，Gan,Yuan，Bo,&Gao，Liang.(2023).Complementary surrogate-assisted differential evolution algorithm for expensive multi-objective problems under a limited computational budget.INFORMATION SCIENCES,632,791-814.

Cai，Xiwen,et al."Complementary surrogate-assisted differential evolution algorithm for expensive multi-objective problems under a limited computational budget".INFORMATION SCIENCES 632(2023):791-814.