Benchmarking large-scale subset selection in evolutionary multi-objective optimization

Shang，Ke; Shu，Tianye; Ishibuchi，Hisao; Nan，Yang; Pang，Lie Meng

2023-04-01

10.1016/j.ins.2022.11.155

INFORMATION SCIENCES   Impact Factor & Quartile

0020-0255

In the field of evolutionary multi-objective optimization (EMO), the standard practice is to present the final population of an EMO algorithm as the output. However, it has been shown that the final population often includes solutions which are dominated by other solutions generated and discarded in previous generations. Recently, a novel EMO framework has been developed to solve this issue by storing all the non-dominated solutions generated during the evolution in an archive, and selecting a subset of solutions from the archive as the output. The key component of this framework is the subset selection from the archive, which typically stores a large number of candidate solutions. However, most relevant studies have focused on small candidate solution sets for environmental selection. There is no benchmark test suite for large-scale subset selection. This study aims to fill this research gap by proposing a benchmark test suite for large-scale subset selection, and providing a comparison between several representative subset selection algorithms using the proposed test suite. The proposed test suite together with the benchmarking studies provides a baseline for researchers to understand, use, compare, and develop large-scale subset selection algorithms in the EMO field.

Benchmarking Evolutionary multi-objective optimization Large-scale subset selection Many-objective optimization

SCI

English

First ; Corresponding

WOS:000900836600005

COMPUTER SCIENCE

2-s2.0-85143853680

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

Shang，Ke,Shu，Tianye,Ishibuchi，Hisao,et al. Benchmarking large-scale subset selection in evolutionary multi-objective optimization[J]. INFORMATION SCIENCES,2023,622:755-770.

Shang，Ke,Shu，Tianye,Ishibuchi，Hisao,Nan，Yang,&Pang，Lie Meng.(2023).Benchmarking large-scale subset selection in evolutionary multi-objective optimization.INFORMATION SCIENCES,622,755-770.

Shang，Ke,et al."Benchmarking large-scale subset selection in evolutionary multi-objective optimization".INFORMATION SCIENCES 622(2023):755-770.