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

Ishibuchi，Hisao; Nan，Yang; Pang，Lie Meng

10.1007/978-3-031-27250-9_24

2023

0302-9743

1611-3349

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

13970 LNCS

333-347

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.

Artificial test problems Evolutionary multi-objective optimization Many-objective optimization Performance evaluation Real-world test problems

First ; Corresponding

English

2-s2.0-85151051933

Scopus

Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China

Ishibuchi，Hisao,Nan，Yang,Pang，Lie Meng. Performance Evaluation of Multi-objective Evolutionary Algorithms Using Artificial and Real-world Problems[C],2023:333-347.