Ensemble R2-based Hypervolume Contribution Approximation

Guotong Wu; Tianye Shu; Yang Nan; Ke Shang; Hisao Ishibuchi

10.1109/SSCI52147.2023.10371823

2023

2770-0097

978-1-6654-3064-7

2023 IEEE Symposium Series on Computational Intelligence (SSCI)

1503-1510

5-8 Dec. 2023

Mexico City, Mexico

The hypervolume-based multi-objective evolutionary algorithms (HV-MOEAs) have proven to be highly effective in solving multi-objective optimization problems. However, the computation time of the hypervolume calculation increases significantly as the number of objectives increases. To address this issue, an R2-based hypervolume contribution approximation (R2-HVC) method was proposed. Nevertheless, the original R2-HVC generates a large number of vectors and computes the HVC only once. In this study, we propose an ensemble method based on the R2-HVC method. By using a small number of vectors for repetitive computation and majority voting, the ensemble method can reduce the probability of making incorrect choices. Experimental results show that the proposed method can improve the approximation accuracy while maintaining a similar computation time to the original R2-HVC method.

Multi-objective Optimization Hypervolume Contribution Ensemble Method Majority Voting

第一

EI

20240415441896

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,Yang Nan,et al. Ensemble R2-based Hypervolume Contribution Approximation[C],2023:1503-1510.