中文版 | English
Title

An Improved Local Search Method for Large-Scale Hypervolume Subset Selection

Author
Publication Years
2022
DOI
Source Title
ISSN
1089-778X
EISSN
1941-0026
VolumePPIssue:99Pages:1-1
Abstract
Hypervolume subset selection (HSS) has received considerable attention in the field of evolutionary multi-objective optimization (EMO). It aims to select a representative subset from a candidate solution set so that the hypervolume of the selected subset is maximized. A number of HSS methods have been proposed in the literature, attempting to either reduce the computation time of subset selection or improve the subset quality (i.e., the hypervolume of the selected subset). However, when selecting from a large candidate set (e.g., from hundreds of thousands of candidate solutions), most HSS methods fail to strike a balance between the computation time and the subset quality. In this paper, we propose a new local search HSS method and its extended version. Three strategies are proposed: The first two strategies are applied to the proposed method to obtain a good subset within a small computation time, and the third one is applied to the extended version to further improve the obtained subset. Experimental results on various candidate sets demonstrate that the proposed method and its extended version are much more efficient and effective than the existing HSS methods.
Keywords
URL[Source Record]
Language
English
SUSTech Authorship
First
ESI Research Field
COMPUTER SCIENCE
Scopus EID
2-s2.0-85141632353
Data Source
Scopus
PDF urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9940313
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/411884
DepartmentDepartment of Computer Science and Engineering
Affiliation
Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Braininspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, China
First Author AffilicationDepartment of Computer Science and Engineering
First Author's First AffilicationDepartment of Computer Science and Engineering
Recommended Citation
GB/T 7714
Nan,Yang,Shang,Ke,Ishibuchi,Hisao,et al. An Improved Local Search Method for Large-Scale Hypervolume Subset Selection[J]. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION,2022,PP(99):1-1.
APA
Nan,Yang,Shang,Ke,Ishibuchi,Hisao,&He,Linjun.(2022).An Improved Local Search Method for Large-Scale Hypervolume Subset Selection.IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION,PP(99),1-1.
MLA
Nan,Yang,et al."An Improved Local Search Method for Large-Scale Hypervolume Subset Selection".IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION PP.99(2022):1-1.
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