Evolutionary algorithm with individual-distribution search strategy and regression-classification surrogates for expensive optimization

Li，Genghui1; Xie，Lindong1; Wang，Zhenkun1,2; Wang，Huajun3; Gong，Maoguo4

2023-07-01

10.1016/j.ins.2023.03.101

Information Sciences   影响因子和分区

0020-0255

1872-6291

Surrogate-assisted evolutionary algorithms (SAEAs) with prescreening model management strategies show great potential in handling expensive optimization problems (EOPs). However, their performance is highly dependent on the search strategy and surrogate model. This paper proposes an evolutionary algorithm called IDRCEA, which utilizes an individual-distribution search strategy (IDS) and a regression-classification-based prescreening mechanism (RCP) to improve the ability to solve various complex and high-dimensional EOPs. Specifically, IDRCEA first combines an individual-based search strategy and a distribution-based search strategy to enrich offspring generation. Then, a regression model and a classification model are cooperatively used to prescreen the high-level offspring. Finally, both performance-based and distribution-based infill criteria are utilized to determine the most promising offspring from the high-level group for expensive evaluation. Experimental results validate the advantages of IDRCEA over some state-of-the-art SAEAs on many complex benchmark problems and an oil reservoir production optimization problem.

Expensive optimization Individual-distribution search strategy Regression-classfication surrogates Surrogate-assisted evolutionary optimization

SCI ; EI

英语

第一 ; 通讯

National Natural Science Foundation of China[62036006];National Natural Science Foundation of China[62106096];National Natural Science Foundation of China[62206120];

Computer Science

Computer Science, Information Systems

WOS:000965552200001

ELSEVIER SCIENCE INC

20231313810872

Optimization ; Petroleum reservoir engineering ; Petroleum reservoirs ; Regression analysis

Oil Fields:512.1.1 ; Petroleum Deposits : Development Operations:512.1.2 ; Optimization Techniques:921.5 ; Mathematical Statistics:922.2

COMPUTER SCIENCE

2-s2.0-85150871131

Scopus

1.School of System Design and Intelligent Manufacturing,Southern University of Science and Technology,Shenzhen,518055,China
2.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.Department of Mathematics and Statistics,Changsha University of Science and Technology,Changsha,410114,China
4.School of Electronic Engineering,Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education,Xidian University,Xi'an,710071,China

Li，Genghui,Xie，Lindong,Wang，Zhenkun,et al. Evolutionary algorithm with individual-distribution search strategy and regression-classification surrogates for expensive optimization[J]. Information Sciences,2023,634:423-442.

Li，Genghui,Xie，Lindong,Wang，Zhenkun,Wang，Huajun,&Gong，Maoguo.(2023).Evolutionary algorithm with individual-distribution search strategy and regression-classification surrogates for expensive optimization.Information Sciences,634,423-442.

Li，Genghui,et al."Evolutionary algorithm with individual-distribution search strategy and regression-classification surrogates for expensive optimization".Information Sciences 634(2023):423-442.