Reference Point Specification for Greedy Hypervolume Subset Selection

Shang，Ke; Ishibuchi，Hisao; Pang，Lie Meng; Nan，Yang

10.1109/SMC52423.2021.9658655

2021

IEEE International Conference on Systems, Man, and Cybernetics (SMC)

1062-922X

978-1-6654-4208-4

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

168-175

OCT 17-20, 2021

null,null,ELECTR NETWORK

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Hypervolume subset selection (HSS) aims to select a subset with a fixed size from a candidate solution set so that the hypervolume of the subset is maximized. The greedy HSS (GHSS) is the most efficient way for solving the HSS problem. When we use GHSS, we implicitly assume that well-distributed solutions over the entire Pareto front will be selected. However, the distribution of selected solutions by GHSS has not been studied. In this paper, we investigate this issue by examining selected solution subsets for different reference point specifications in GHSS. First, we show that a sufficiently large reference point is a good choice for GHSS to select a well-distributed subset on a triangular Pareto front. However, it is not easy to properly specify a reference point for an inverted triangular Pareto front. Then, we propose a dynamic reference point specification method for GHSS to select a well-distributed subset for various types of Pareto fronts. Static and dynamic reference point specifications are compared through computational experiments using 3- and 5-objective candidate solution sets from various types of Pareto front shapes. The experimental results demonstrate the effect of different reference point specifications on the subsets selected by GHSS and the usefulness of the dynamic reference point specification for GHSS.

Shape Conferences Cybernetics

第一 ; 通讯

英语

CPCI-S ; EI

National Natural Science Foundation of China[61876075];National Natural Science Foundation of China[62002152];

Computer Science

Computer Science, Cybernetics ; Computer Science, Information Systems

WOS:000800532000023

20220711617254

Evolutionary algorithms ; Set theory

Codes and Standards:902.2 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4

2-s2.0-85124311381

Scopus

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

Shang，Ke,Ishibuchi，Hisao,Pang，Lie Meng,et al. Reference Point Specification for Greedy Hypervolume Subset Selection[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021:168-175.