Counterintuitive Experimental Results in Evolutionary Large-Scale Multi-Objective Optimization

Pang，Lie Meng; Ishibuchi，Hisao; Shang，Ke

2022

10.1109/TEVC.2022.3161363

IEEE Transactions on Evolutionary Computation   影响因子和分区

1941-0026

1941-0026

Recently, large-scale multiobjective optimization has received increasing attention from the evolutionary multiobjective optimization (EMO) community. This has led to the emergence of a specialized research area called evolutionary large-scale multiobjective optimization (ELMO). In general, it is believed that multiobjective optimization problems become more difficult as the number of decision variables increases. However, the following two counterintuitive observations are obtained from careful examinations of recent ELMO studies. One is that experimental results on some large-scale multiobjective test problems were improved by increasing the number of decision variables. The other is that better results were obtained for some other large-scale multiobjective test problems by conventional EMO algorithms (EMOAs) than state-of-the-art ELMO algorithms (ELMOAs). These observations suggest that ELMOAs have not always been evaluated on appropriate test problems. Moreover, their performance is not always better than the performance of conventional EMOAs. In this letter, we first re-examine the performance of ELMOAs and conventional EMOAs on a wide variety of scalable multiobjective test problems. Then, counterintuitive experimental results are analyzed using the anytime performance evaluation scheme and distributions of randomly generated initial solutions. Based on the analysis, suggestions on how to handle large-scale multiobjective test problems with counterintuitive results are proposed.

Anytime performance evaluation evolutionary multi-objective optimization large-scale multi-objective optimization test problems.

SCI ; EI

英语

第一 ; 通讯

National Natural Science Foundation of China["61876075","62002152"] ; Guangdong Provincial Key Laboratory[2020B121201001] ; Program for Guangdong Introducing Innovative and Enterpreneurial Teams[2017ZT07X386] ; Stable Support Plan Program of Shenzhen Natural Science Fund[20200925174447003] ; Shenzhen Science and Technology Program[KQTD2016112514355531]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods

WOS:000892933300032

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

20221311864117

Benchmarking ; Decision making ; Evolutionary algorithms ; Pareto principle ; Testing

Management:912.2 ; Optimization Techniques:921.5

COMPUTER SCIENCE

IEEE

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

Pang，Lie Meng,Ishibuchi，Hisao,Shang，Ke. Counterintuitive Experimental Results in Evolutionary Large-Scale Multi-Objective Optimization[J]. IEEE Transactions on Evolutionary Computation,2022,PP(99):1-1.

Pang，Lie Meng,Ishibuchi，Hisao,&Shang，Ke.(2022).Counterintuitive Experimental Results in Evolutionary Large-Scale Multi-Objective Optimization.IEEE Transactions on Evolutionary Computation,PP(99),1-1.

Pang，Lie Meng,et al."Counterintuitive Experimental Results in Evolutionary Large-Scale Multi-Objective Optimization".IEEE Transactions on Evolutionary Computation PP.99(2022):1-1.