Approximating Hypervolume and Hypervolume Contributions Using Polar Coordinate

被引：37
|
作者：
Deng, Jingda [1 ]
Zhang, Qingfu [1 ]
机构：
[1] City Univ Hong Kong, Coll Sci & Engn, Dept Comp Sci, Hong Kong, Peoples R China
来源：
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION | 2019年 / 23卷 / 05期
关键词：
Approximation methods; Approximation algorithms; Monte Carlo methods; Manganese; Pareto optimization; Transforms; hypervolume; hypervolume contribution; multiobjective optimization; EVOLUTIONARY ALGORITHMS;
D O I：
10.1109/TEVC.2019.2895108
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The hypervolume and hypervolume contributions are widely used in multiobjective evolutionary optimization. However, their exact calculation is NP-hard. By definition, hypervolume is anis the number of objectives). Using polar coordinate, this paper transforms the hypervolume into an -D integral, and then proposes two approximation methods for computing the hypervolume and hypervolume contributions. Numerical experiments have been conducted to investigate the performance of our proposed methods.
引用
收藏
页码：913 / 918
页数：6
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