南方科技大学知识苑(SUSTech KC): What is a good direction vector set for the R2-based hypervolume contribution approximation

题名	What is a good direction vector set for the R2-based hypervolume contribution approximation
作者	Nan，Yang; Shang，Ke; Ishibuchi，Hisao
通讯作者	Ishibuchi，Hisao
DOI	10.1145/3377930.3390171
发表日期	2020-06-25
会议录名称	GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference
页码	524-532
摘要	The hypervolume contribution is an important concept in hypervolume-based evolutionary multi-objective optimization algorithms. It describes the loss of the hypervolume when a solution is removed from the current population. Since its calculation is #P-hard in the number of objectives, its approximation is necessary for many-objective optimization problems. Recently, an R2-based hypervolume contribution approximation method was proposed. This method relies on a set of direction vectors for the approximation. However, the influence of different direction vector generation methods on the approximation quality has not been studied yet. This paper aims to investigate this issue. Five direction vector generation methods are investigated, including Das and Dennis's method (DAS), unit normal vector method (UNV), JAS method, maximally sparse selection method with DAS (MSS-D), and maximally sparse selection method with UNV (MSS-U). Experimental results suggest that the approximation quality strongly depends on the direction vector generation method. The JAS and UNV methods show the best performance whereas the DAS method shows the worst performance. The reasons behind the results are also analyzed.
关键词	Direction vectors Evolutionary multi-objective optimization Hypervolume contribution approximation
学校署名	第一 ; 通讯
语种	英语
相关链接	[Scopus记录]
收录类别	EI
EI入藏号	20204009295559
EI主题词	Vectors ; Satellites ; Evolutionary algorithms
EI分类号	Satellites:655.2 ; Algebra:921.1 ; Optimization Techniques:921.5
Scopus记录号	2-s2.0-85091794650
来源库	Scopus
引用统计	被引频次[WOS]：5
成果类型	会议论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/187978
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,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	Nan，Yang,Shang，Ke,Ishibuchi，Hisao. What is a good direction vector set for the R2-based hypervolume contribution approximation[C],2020:524-532.