Normalization in R2-Based Hypervolume and Hypervolume Contribution Approximation

Guotong Wu; Tianye Shu; Ke Shang; Hisao Ishibuchi

10.1109/SSCI52147.2023.10371986

2023

2770-0097

978-1-6654-3064-7

2023 IEEE Symposium Series on Computational Intelligence (SSCI)

449-456

5-8 Dec. 2023

Mexico City, Mexico

In this paper, we examine the effect of normalization in R2-based hypervolume and hypervolume contribution approximation. The fact is that the region with different scales on objective space brings approximation bias. The basic idea of normalization is to perform a coordinate transformation to make the shape of the approximated region more regular, and then transform it to obtain the final value according to the property of hypervolume and hypervolume contribution. The performance of normalization is evaluated on different datasets by comparing it with the original R2-based method. We use two different metrics to evaluate hypervolume and hypervolume contribution separately, and the results indicate that normalization does exactly improve the approximation accuracy and outperforms the original R2-based method.

─Multi-objective Optimization Hypervolume Normalization

第一

EI

20240415442059

IEEE

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

Guotong Wu,Tianye Shu,Ke Shang,et al. Normalization in R2-Based Hypervolume and Hypervolume Contribution Approximation[C],2023:449-456.