Title | Performance Evaluation of Multi-objective Evolutionary Algorithms Using Artificial and Real-world Problems |
Author | |
Corresponding Author | Ishibuchi,Hisao |
DOI | |
Publication Years | 2023
|
ISSN | 0302-9743
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EISSN | 1611-3349
|
Source Title | |
Volume | 13970 LNCS
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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
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Document Type | Conference paper |
Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/524289 |
Department | Department 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 Affilication | Department of Computer Science and Engineering |
Corresponding Author Affilication | Department of Computer Science and Engineering |
First Author's First Affilication | Department 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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