Improving Local Search Hypervolume Subset Selection in Evolutionary Multi-objective Optimization

Nan，Yang; Shang，Ke; Ishibuchi，Hisao; He，Linjun

10.1109/SMC52423.2021.9659147

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

751-757

OCT 17-20, 2021

null,null,ELECTR NETWORK

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

IEEE

Hypervolume subset selection is a hot topic in the field of evolutionary multi-objective optimization (EMO) due to the increasing needs of selecting a small set of representative solutions from a large set of non-dominated solutions (e.g., unbounded external archive). To maximize the hypervolume (HV) of the selected subset, a number of HV subset selction (HSS) methods have been proposed. Greedy forward selection (GFS) subset selection method is the most popular one, which has been actively investigated in the literature. However, few studies focus on local search (LS) HSS method, which is similar to the mechanism of SMS-EMOA. The time cost of the LS method is usually high, and the quality of the subset selected by this method is always poor. To address these two issues, in this paper, we first adopt an HV contribution update strategy to the original LS method to significantly reduce its time cost. In addition, two efficient strategies are proposed to improve the performance of the LS method to get a better subset. Finally, experiments are conducted to show the effectiveness of the improved LS method.

Costs Conferences Optimization 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:000800532000116

20220711617167

Evolutionary algorithms ; Local search (optimization) ; Multiobjective optimization

Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Optimization Techniques:921.5

2-s2.0-85124277319

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

Nan，Yang,Shang，Ke,Ishibuchi，Hisao,et al. Improving Local Search Hypervolume Subset Selection in Evolutionary Multi-objective Optimization[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021:751-757.