HVC-Net: Deep Learning Based Hypervolume Contribution Approximation

Shang，Ke; Liao，Weiduo; Ishibuchi，Hisao

10.1007/978-3-031-14714-2_29

2022

17th International Conference on Parallel Problem Solving from Nature (PPSN)

0302-9743

1611-3349

978-3-031-14713-5

LECT NOTES ARTIF INT

13398 LNCS

414-426

SEP 10-14, 2022

null,Dortmund,GERMANY

GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND

SPRINGER INTERNATIONAL PUBLISHING AG

In this paper, we propose HVC-Net, a deep learning based hypervolume contribution approximation method for evolutionary multi-objective optimization. The basic idea of HVC-Net is to use a deep neural network to approximate the hypervolume contribution of each solution in a non-dominated solution set. HVC-Net has two characteristics: (1) It is permutation equivalent to the order of solutions in the input solution set, and (2) a single HVC-Net can handle solution sets of various size (e.g., solution sets with 20, 50 and 100 solutions). The performance of HVC-Net is evaluated through computational experiments by comparing it with two commonly-used hypervolume contribution approximation methods (i.e., point-based method and line-based method). Our experimental results show that HVC-Net outperforms the other two methods in terms of both the runtime and the ability to identify the smallest (largest) hypervolume contributor in a solution set, which shows the superiority of HVC-Net for hypervolume contribution approximation.

Approximation Deep learning Evolutionary multi-objective optimization Hypervolume contribution

第一 ; 通讯

英语

EI ; CPCI-S

National Natural Science Foundation of China["62002152","61876075"]

Computer Science

Computer Science, Artificial Intelligence

WOS:000871752100029

20223512669284

Approximation theory ; Evolutionary algorithms ; Multiobjective optimization

Ergonomics and Human Factors Engineering:461.4 ; Optimization Techniques:921.5 ; Numerical Methods:921.6

2-s2.0-85136924801

Scopus

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

Shang，Ke,Liao，Weiduo,Ishibuchi，Hisao. HVC-Net: Deep Learning Based Hypervolume Contribution Approximation[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2022:414-426.