Dynamic Normalization in MOEA/D for Multiobjective optimization

He，Linjun1; Ishibuchi，Hisao1; Trivedi，Anupam2; Srinivasan，Dipti2

10.1109/CEC48606.2020.9185849

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

Objective space normalization is important since areal-world multiobjective problem usually has differently scaled objective functions. Recently, bad effects of the commonly used simple normalization method have been reported for the popular decomposition-based algorithm MOEA/D. However, the effects of recently proposed sophisticated normalization methods have not been investigated. In this paper, we examine the effectiveness of these normalization methods in MOEA/D. We find that these normalization methods can cause performance deterioration. We also find that the sophisticated normalization methods are not necessarily better than the simple one. Although the negative effects of inaccurate estimation of the nadir point are well recognized in the literature, no solution has been proposed. In order to address this issue, we propose two dynamic normalization strategies which dynamically adjust the extent of normalization during the evolutionary process. Experimental results clearly show the necessity of considering the extent of normalization.

decomposition based algorithms evolutionary multiobjective optimization MOEA/D Objective space normalization

第一

英语

EI ; CPCI-S

National Natural Science Foundation of China[61876075] ; Program for Guangdong Introducing Innovative and Enterpreneurial Teams[2017ZT07X386] ; Shenzhen Peacock Plan[KQTD2016112514355531] ; Science and Technology Innovation Committee Foundation of Shenzhen[ZDSYS201703031748284] ; Program for University Key Laboratory of Guangdong Province[2017KSYS008]

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:000703998202106

20204109316785

Evolutionary algorithms ; Deterioration

Optimization Techniques:921.5 ; Materials Science:951

2-s2.0-85092048128

Scopus

1.University Key Laboratory of Evolving Intelligent Systems of Guangdong Province,Southern University of Science and Technology,Shenzhen Key Laboratory of Computational Intelligence,Shenzhen,China
2.National University of Singapore,Department of Electrical and Computer Engineering,Singapore

He，Linjun,Ishibuchi，Hisao,Trivedi，Anupam,et al. Dynamic Normalization in MOEA/D for Multiobjective optimization[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2020:1-8.