A Survey on the Hypervolume Indicator in Evolutionary Multiobjective Optimization

被引:142
|
作者
Shang, Ke [1 ]
Ishibuchi, Hisao [1 ]
He, Linjun [1 ]
Pang, Lie Meng [1 ]
机构
[1] Southern Univ Sci & Technol, Dept Comp Sci & Engn, Guangdong Prov Key Lab Brain Inspired Intelligent, Shenzhen 518055, Peoples R China
基金
中国国家自然科学基金;
关键词
Evolutionary algorithms; hypervolume contribution; hypervolume indicator; many-objective optimization; multiobjective optimization; COVARIANCE-MATRIX ADAPTATION; SUBSET-SELECTION; ALGORITHM; APPROXIMATIONS;
D O I
10.1109/TEVC.2020.3013290
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Hypervolume is widely used as a performance indicator in the field of evolutionary multiobjective optimization (EMO). It is used not only for performance evaluation of EMO algorithms (EMOAs) but also in indicator-based EMOAs to guide the search. Since its initial proposal in the late 1990s, a wide variety of studies have been done on various topics, including hypervolume calculation, optimal $\mu $ -distribution, subset selection, hypervolume-based EMOAs, and extensions of the hypervolume indicator. However, currently there is no work to systematically survey the hypervolume indicator for these topics whereas it has been frequently used in the EMO field. This article aims to fill this gap and provide a comprehensive survey on the hypervolume indicator. We expect that this survey will help EMO researchers to understand the hypervolume indicator more deeply and thoroughly, and promote further utilization of the hypervolume indicator in the EMO field.
引用
收藏
页码:1 / 20
页数:20
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