Learning to Approximate: Auto Direction Vector Set Generation for Hypervolume Contribution Approximation

Ke Shang; Tianye Shu; Hisao Ishibuchi

2022

10.1109/TEVC.2022.3230828

IEEE Transactions on Evolutionary Computation   影响因子和分区

1941-0026

1941-0026

Hypervolume contribution is an important concept in evolutionary multiobjective optimization (EMO). It involves hypervolume-based EMO algorithms and hypervolume subset selection algorithms. Its main drawback is that it is computationally expensive in high-dimensional spaces, which limits its applicability to many-objective optimization. Recently, an R2 indicator variant (i.e., R2HVC indicator) is proposed to approximate the hypervolume contribution. The R2HVC indicator uses line segments along a number of direction vectors for hypervolume contribution approximation. It has been shown that different direction vector sets lead to different approximation qualities. In this article, we propose learning to approximate (LtA), a direction vector set generation method for the R2HVC indicator. The direction vector set is automatically learned from training data. The learned direction vector set can then be used in the R2HVC indicator to improve its approximation quality. The usefulness of the proposed LtA method is examined by comparing it with other commonly used direction vector set generation methods for the R2HVC indicator. Experimental results suggest the superiority of LtA over the other methods for generating high-quality direction vector sets.

Hypervolume indicator Hypervolume contribution Evolutionary multi-objective optimization Approximation

SCI ; EI

英语

第一

COMPUTER SCIENCE

2-s2.0-85146232550

IEEE

Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, China

Ke Shang,Tianye Shu,Hisao Ishibuchi. Learning to Approximate: Auto Direction Vector Set Generation for Hypervolume Contribution Approximation[J]. IEEE Transactions on Evolutionary Computation,2022,PP(99):1-1.

Ke Shang,Tianye Shu,&Hisao Ishibuchi.(2022).Learning to Approximate: Auto Direction Vector Set Generation for Hypervolume Contribution Approximation.IEEE Transactions on Evolutionary Computation,PP(99),1-1.

Ke Shang,et al."Learning to Approximate: Auto Direction Vector Set Generation for Hypervolume Contribution Approximation".IEEE Transactions on Evolutionary Computation PP.99(2022):1-1.