Direction Vector Selection for R2-Based Hypervolume Contribution Approximation

Shu，Tianye; Shang，Ke; Nan，Yang; Ishibuchi，Hisao

10.1007/978-3-031-14721-0_8

2022

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

0302-9743

1611-3349

978-3-031-14720-3

LECT NOTES ARTIF INT

13399 LNCS

110-123

SEP 10-14, 2022

null,Dortmund,GERMANY

GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND

SPRINGER INTERNATIONAL PUBLISHING AG

Recently, an R2-based hypervolume contribution approximation (i.e., R2HVC indicator) has been proposed and applied to evolutionary multi-objective algorithms and subset selection. The R2HVC indicator approximates the hypervolume contribution using a set of line segments determined by a direction vector set. Although the R2HVC indicator is computationally efficient compared with the exact hypervolume contribution calculation, its approximation error is large if an inappropriate direction vector set is used. In this paper, we propose a method to generate a direction vector set for reducing the approximation error of the R2HVC indicator. The method generates a set of direction vectors by selecting a small direction vector set from a large candidate direction vector set in a greedy manner. Experimental results show that the proposed method outperforms six existing direction vector set generation methods. The direction vector set generated by the proposed method can be further used to improve the performance of hypervolume-based algorithms which rely on the R2HVC indicator.

Evolutionary multi-objective optimization Hypervolume contribution Hypervolume contribution approximation

First ; Corresponding

English

EI ; CPCI-S

National Natural Science Foundation of China["62002152","61876075"] ; Guangdong Provincial Key Laboratory[2020B121201001] ; Program for Guangdong Introducing Innovative and Enterpreneurial Teams[2017ZT07X386] ; Stable Support Plan Program of Shenzhen Natural Science Fund[20200925174447003] ; Shenzhen Science and Technology Program[KQTD2016112514355531]

Computer Science

Computer Science, Artificial Intelligence

WOS:000871753400008

20223712707360

Approximation algorithms ; Genetic algorithms ; Vectors

Mathematics:921 ; Algebra:921.1 ; Optimization Techniques:921.5

2-s2.0-85137270657

Scopus

Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China

Shu，Tianye,Shang，Ke,Nan，Yang,et al. Direction Vector Selection for R2-Based Hypervolume Contribution Approximation[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2022:110-123.