中文版 | English
Title

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

Author
Corresponding AuthorWang,Zhenkun
Publication Years
2023-07-01
DOI
Source Title
ISSN
0020-0255
Volume634Pages:423-442
Abstract
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.
Keywords
URL[Source Record]
Language
English
SUSTech Authorship
First ; Corresponding
Funding Project
National Natural Science Foundation of China[62036006];National Natural Science Foundation of China[62106096];National Natural Science Foundation of China[62206120];
ESI Research Field
COMPUTER SCIENCE
Scopus EID
2-s2.0-85150871131
Data Source
Scopus
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/524101
DepartmentSchool of System Design and Intelligent Manufacturing
工学院_计算机科学与工程系
Affiliation
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
First Author AffilicationSchool of System Design and Intelligent Manufacturing
Corresponding Author AffilicationSchool of System Design and Intelligent Manufacturing;  Department of Computer Science and Engineering
First Author's First AffilicationSchool of System Design and Intelligent Manufacturing
Recommended Citation
GB/T 7714
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.
APA
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.
MLA
Li,Genghui,et al."Evolutionary algorithm with individual-distribution search strategy and regression-classification surrogates for expensive optimization".Information Sciences 634(2023):423-442.
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