A Study of the Naive Objective Space Normalization Method in MOEA/D

He, Linjun1,2; Nan, Yang1; Shang, Ke1; Ishibuchi, Hisao1

2019

2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019)

1834-1840

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

IEEE

Complex Pareto fronts with objectives in different scales usually appear in real-world multi-objective optimization problems. So as to treat different objectives equally, the naive normalization method is frequently used due to its simplicity in calculating the estimated ideal and nadir points (i.e., without generating a hyperplane). By directly making use of information from the obtained solutions, the estimated ideal point and the estimated nadir point are obtained. However, the naive normalization method has rarely been investigated. Moreover, its formulation is often different in each study in the literature. In this paper, we first show four different formulations of the naive normalization. They are based on different estimation mechanisms of the ideal point and the nadir point. Next we investigate the effect of each formulation on the performance of MOEA/D. Our results show that the search behavior of MOEA/D is significantly impacted due to the choice of a formulation of the naive normalization method. Finally we suggest the most effective formulation of the naive normalization method for MOEA/D.

Decomposition-based algorithm multi-objective optimization objective space normalization many-objective optimization ideal point nadir point

第一 ; 通讯

英语

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

Computer Science, Artificial Intelligence

WOS:000555467201135

Web of Science

1.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen Key Lab Computat Intelligence, Univ Key Lab Evolving Intelligent Syst Guangdong, Shenzhen, Peoples R China
2.Natl Univ Singapore, Dept Elect & Comp Engn, Singapore, Singapore

He, Linjun,Nan, Yang,Shang, Ke,et al. A Study of the Naive Objective Space Normalization Method in MOEA/D[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2019:1834-1840.