On the Normalization in Evolutionary Multi-Modal Multi-Objective Optimization

Liu，Yiping1; Ishibuchi，Hisao2; Yen，Gary G.3; Nojima，Yusuke1; Masuyama，Naoki1; Han，Yuyan4

10.1109/CEC48606.2020.9185899

2020-07-01

IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

978-1-7281-6930-9

2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings

1-8

JUL 19-24, 2020

null,null,ELECTR NETWORK

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Multi-modal multi-objective optimization problems may have different Pareto optimal solutions with the same objective vector. A number of evolutionary multi-modal multiobjective algorithms have been developed to solve these problems. They aim to search for a Pareto optimal solution set with good diversity in both the objective and decision spaces. Although the normalization in both the objective and decision spaces is very important for these algorithms, there are few studies on this topic. In this paper, we investigate the effect of four normalization methods on two evolutionary multi-modal multiobjective algorithms. Six distance minimization problems are chosen as test problems. The experimental results show that the effect of normalization in evolutionary multi-modal multiobjective optimization is algorithm- and problem-dependent.

decision space multi-modal multi-objective evolutionary optimization normalization objective space

其他

英语

EI ; CPCI-S

National Natural Science Foundation of China[61876075,61803192] ; Guangdong Provincial Key Laboratory[2020B121201001] ; Program for Guangdong Introducing Innovative and Enterpreneurial Teams[2017ZT07X386] ; Shenzhen Science and Technology Program[KQTD2016112514355531] ; Program for University Key Laboratory of Guangdong Province[2017KSYS008] ; Japan Society for the Promotion of Science (JSPS) KAKENHI[JP19K20358]

Computer Science ; Engineering ; Mathematical & Computational Biology ; Operations Research & Management Science

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic ; Mathematical & Computational Biology ; Operations Research & Management Science

WOS:000703998203028

20204109316466

Evolutionary algorithms ; Optimal systems ; Pareto principle

Optimization Techniques:921.5 ; Systems Science:961

2-s2.0-85092056775

Scopus

1.Osaka Prefecture University,Department of Computer Science and Intelligent Systems,Sakai, Osaka,599-8531,Japan
2.Southern University of Science and Technology,Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Shenzhen,518055,China
3.Oklahoma State University,School of Electrical and Computer Engineering,Stillwater,74078,United States
4.Liaocheng University,School of Computer Science,Liaocheng,252059,China

Liu，Yiping,Ishibuchi，Hisao,Yen，Gary G.,et al. On the Normalization in Evolutionary Multi-Modal Multi-Objective Optimization[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2020:1-8.