Empirical Hypervolume Optimal µ-Distributions on Complex Pareto Fronts

Ke Shang; Tianye Shu; Guotong Wu; Yang Nan; Lie Meng Pang; Hisao Ishibuchi

10.1109/SSCI52147.2023.10372016

2023

2770-0097

978-1-6654-3064-7

2023 IEEE Symposium Series on Computational Intelligence (SSCI)

433-440

5-8 Dec. 2023

Mexico City, Mexico

Hypervolume optimal µ-distribution is the distribution of µ solutions maximizing the hypervolume indicator of µ solutions on a specific Pareto front. Most studies have focused on simple Pareto fronts such as triangular and inverted triangular Pareto fronts. There is almost no study which focuses on complex Pareto fronts such as disconnected and partially degenerate Pareto fronts. However, most real-world multi-objective optimization problems have such a complex Pareto front. Thus, it is of great practical significance to study the hypervolume optimal µ-distribution on the complex Pareto fronts. In this paper, we study this issue by empirically showing the hypervolume optimal µ-distributions on the Pareto fronts of some representative artificial and real-world test problems. Our results show that, in general, maximizing the hypervolume indicator does not lead to uniformly distributed solution sets on the complex Pareto fronts. We also give some suggestions related to the use of the hypervolume indicator for performance evaluation of evolutionary multi-objective optimization algorithms.

Hypervolume optimal µ-distribution complex Pareto front multi-objective optimization

第一

EI

20240415441989

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,Guotong Wu,et al. Empirical Hypervolume Optimal µ-Distributions on Complex Pareto Fronts[C],2023:433-440.