R2-Based Hypervolume Contribution Approximation

被引:38
作者
Shang, Ke [1 ]
Ishibuchi, Hisao [1 ]
机构
[1] Southern Univ Sci & Technol, Univ Key Lab Evolving Intelligent Syst Guangdong, Dept Comp Sci & Engn, Shenzhen Key Lab Computat Intelligence, Shenzhen 518055, Peoples R China
基金
中国国家自然科学基金;
关键词
Monte Carlo methods; Optimization; Nickel; Approximation methods; Sociology; Indexes; Evolutionary multiobjective optimization (EMO); hypervolume contribution; R2; indicator; ALGORITHM;
D O I
10.1109/TEVC.2019.2909271
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this letter, a new hypervolume contribution approximation method is proposed which is formulated as an R2 indicator. The basic idea of the proposed method is to use different line segments only in the hypervolume contribution region for the hypervolume contribution approximation. Comparing with a traditional method which is based on the R2 indicator to approximate the hypervolume, the new method can directly approximate the hypervolume contribution and will utilize all the direction vectors only in the hypervolume contribution region. The new method, the traditional method, and the Monte Carlo sampling method together with two exact methods are compared through comprehensive experiments. Our results show the advantages of the new method over the other methods. Comparing with the other two approximation methods, the new method achieves the best performance for comparing hypervolume contributions of different solutions and identifying the solution with the smallest hypervolume contribution. Comparing with the exact methods, the new method is computationally efficient in high-dimensional spaces where the exact methods are impractical to use.
引用
收藏
页码:185 / 192
页数:8
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