南方科技大学知识苑(SUSTech KC): Simultaneous use of two normalization methods in decomposition-based multi-objective evolutionary algorithms

题名	Simultaneous use of two normalization methods in decomposition-based multi-objective evolutionary algorithms
作者	He，Linjun; Shang，Ke; Ishibuchi，Hisao
通讯作者	Ishibuchi，Hisao
发表日期	2020-07-01
DOI	10.1016/j.asoc.2020.106316
发表期刊	APPLIED SOFT COMPUTING 影响因子和分区
ISSN	1568-4946
EISSN	1872-9681
卷号	92
摘要	In real-world applications, the order of magnitude in each objective varies, whereas most of fitness evaluation methods in many-objective solvers are scaling dependent. Objective space normalization has a large effect on the performance of each algorithm (i.e., on the practical applicability of each algorithm to real-world problems). In order to put equal emphasis on each objective, a normalization mechanism is always encouraged to be employed in the framework of the algorithm. Decomposition-based algorithms have become more and more popular in many-objective optimization. MOEA/D is a representative decomposition-based algorithm. Recently, some negative effects of normalization have been reported, which may deteriorate the practical applicability of MOEA/D to real-world problems. In this paper, to remedy the performance deterioration introduced by normalization in MOEA/D, we propose an idea of using two types of normalization methods in MOEA/D simultaneously (denoted as MOEA/D-2N). The proposed idea is compared with the standard MOEA/D and MOEA/D with normalization (denoted as MOEA/D-N) via two widely-used test suites (as well as their variants) and a real-world optimization problem. Experimental results show that MOEA/D-2N can effectively evolve a more diverse set of solutions and achieve robust and comparable performance compared with the standard MOEA/D and MOEA/D-N.
关键词	Decomposition-based algorithm Evolutionary multi-objective optimization (EMO) Many-objective optimization MOEA/D Objective space normalization
相关链接	[Scopus记录]
收录类别	SCI ; EI
语种	英语
学校署名	第一 ; 通讯
资助项目	National Natural Science Foundation of China[61876075] ; 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]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS记录号	WOS:000537255300009
出版者	ELSEVIER
EI入藏号	20201808587029
EI主题词	Evolutionary algorithms ; Deterioration
EI分类号	Optimization Techniques:921.5 ; Materials Science:951
ESI学科分类	COMPUTER SCIENCE
Scopus记录号	2-s2.0-85083757133
来源库	Scopus
引用统计	被引频次[WOS]：12
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/138109
专题	工学院_计算机科学与工程系
作者单位	Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
第一作者单位	计算机科学与工程系
通讯作者单位	计算机科学与工程系
第一作者的第一单位	计算机科学与工程系
推荐引用方式 GB/T 7714	He，Linjun,Shang，Ke,Ishibuchi，Hisao. Simultaneous use of two normalization methods in decomposition-based multi-objective evolutionary algorithms[J]. APPLIED SOFT COMPUTING,2020,92.
APA	He，Linjun,Shang，Ke,&Ishibuchi，Hisao.(2020).Simultaneous use of two normalization methods in decomposition-based multi-objective evolutionary algorithms.APPLIED SOFT COMPUTING,92.
MLA	He，Linjun,et al."Simultaneous use of two normalization methods in decomposition-based multi-objective evolutionary algorithms".APPLIED SOFT COMPUTING 92(2020).