中文版 | English
Title

Performance Evaluation of Multi-objective Evolutionary Algorithms Using Artificial and Real-world Problems

Author
Corresponding AuthorIshibuchi,Hisao
DOI
Publication Years
2023
ISSN
0302-9743
EISSN
1611-3349
Source Title
Volume
13970 LNCS
Pages
333-347
Abstract
Performance of evolutionary multi-objective optimization (EMO) algorithms is usually evaluated using artificial test problems such as DTLZ and WFG. Every year, new EMO algorithms with high performance on those test problems are proposed. One question is whether they also work well on real-world problems. In this paper, we try to find an answer to this question by examining the performance of ten EMO algorithms including both well-known representative algorithms and recently-proposed new algorithms. First, those algorithms are applied to five artificial test suites (DTLZ, WFG, Minus-DTLZ, Minus-WFG and MaF) and three real-world problem suites. The performance of each algorithm is evaluated by the hypervolume indicator. Next, the ranking of the ten EMO algorithms is created for each problem suite. That is, eight different rankings are obtained (each ranking is for each problem suite). Then, the eight different rankings are visually compared to answer our research question. The distance between two rankings is also calculated to support visual comparison results. Our experimental results show that similar rankings of the ten EMO algorithms are obtained for the three real-world problem suites and Minus-WFG. It is also shown that the ranking for each of the three real-world problem suites is clearly different from their ranking for DTLZ.
Keywords
SUSTech Authorship
First ; Corresponding
Language
English
URL[Source Record]
Scopus EID
2-s2.0-85151051933
Data Source
Scopus
Citation statistics
Cited Times [WOS]:0
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/524289
DepartmentDepartment of Computer Science and Engineering
Affiliation
Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
First Author AffilicationDepartment of Computer Science and Engineering
Corresponding Author AffilicationDepartment of Computer Science and Engineering
First Author's First AffilicationDepartment of Computer Science and Engineering
Recommended Citation
GB/T 7714
Ishibuchi,Hisao,Nan,Yang,Pang,Lie Meng. Performance Evaluation of Multi-objective Evolutionary Algorithms Using Artificial and Real-world Problems[C],2023:333-347.
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