CASIA OpenIR  > 学术期刊  > IEEE/CAA Journal of Automatica Sinica
Evolutionary Optimization Methods for High-Dimensional Expensive Problems: A Survey
MengChu Zhou; Meiji Cui; Dian Xu; Shuwei Zhu; Ziyan Zhao; Abdullah Abusorrah
Source PublicationIEEE/CAA Journal of Automatica Sinica
AbstractEvolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization problems. The past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems (HEPs). The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer simulations. Moreover, it is hard to traverse the huge search space within reasonable resource as problem dimension increases. Traditional evolutionary algorithms (EAs) tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory results. To reduce such evaluations, many novel surrogate-assisted algorithms emerge to cope with HEPs in recent years. Yet there lacks a thorough review of the state of the art in this specific and important area. This paper provides a comprehensive survey of these evolutionary algorithms for HEPs. We start with a brief introduction to the research status and the basic concepts of HEPs. Then, we present surrogate-assisted evolutionary algorithms for HEPs from four main aspects. We also give comparative results of some representative algorithms and application examples. Finally, we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.
KeywordEvolutionary algorithm (EA) high-dimensional expensive problems (HEPs) industrial applications surrogate-assisted optimization
Citation statistics
Document Type期刊论文
Collection学术期刊_IEEE/CAA Journal of Automatica Sinica
Recommended Citation
GB/T 7714
MengChu Zhou,Meiji Cui,Dian Xu,et al. Evolutionary Optimization Methods for High-Dimensional Expensive Problems: A Survey[J]. IEEE/CAA Journal of Automatica Sinica,2024,11(5):1092-1105.
APA MengChu Zhou,Meiji Cui,Dian Xu,Shuwei Zhu,Ziyan Zhao,&Abdullah Abusorrah.(2024).Evolutionary Optimization Methods for High-Dimensional Expensive Problems: A Survey.IEEE/CAA Journal of Automatica Sinica,11(5),1092-1105.
MLA MengChu Zhou,et al."Evolutionary Optimization Methods for High-Dimensional Expensive Problems: A Survey".IEEE/CAA Journal of Automatica Sinica 11.5(2024):1092-1105.
Files in This Item: Download All
File Name/Size DocType Version Access License
JAS-2023-1088.pdf(2100KB)期刊论文出版稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[MengChu Zhou]'s Articles
[Meiji Cui]'s Articles
[Dian Xu]'s Articles
Baidu academic
Similar articles in Baidu academic
[MengChu Zhou]'s Articles
[Meiji Cui]'s Articles
[Dian Xu]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[MengChu Zhou]'s Articles
[Meiji Cui]'s Articles
[Dian Xu]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: JAS-2023-1088.pdf
Format: Adobe PDF
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.