An Improved Local Search Method for Large-Scale Hypervolume Subset Selection

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

2022

10.1109/TEVC.2022.3219081

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION   影响因子和分区

1089-778X

1941-0026

Hypervolume subset selection (HSS) has received considerable attention in the field of evolutionary multi-objective optimization (EMO). It aims to select a representative subset from a candidate solution set so that the hypervolume of the selected subset is maximized. A number of HSS methods have been proposed in the literature, attempting to either reduce the computation time of subset selection or improve the subset quality (i.e., the hypervolume of the selected subset). However, when selecting from a large candidate set (e.g., from hundreds of thousands of candidate solutions), most HSS methods fail to strike a balance between the computation time and the subset quality. In this paper, we propose a new local search HSS method and its extended version. Three strategies are proposed: The first two strategies are applied to the proposed method to obtain a good subset within a small computation time, and the third one is applied to the extended version to further improve the obtained subset. Experimental results on various candidate sets demonstrate that the proposed method and its extended version are much more efficient and effective than the existing HSS methods.

evolutionary multi-objective optimization (EMO) Hypervolume subset selection (HSS) local search (LS) many-objective optimization

EI ; SCI

英语

第一

20224613111867

Evolutionary algorithms ; Feature Selection ; Local search (optimization) ; Set theory

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

COMPUTER SCIENCE

2-s2.0-85141632353

Scopus

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

Nan，Yang,Shang，Ke,Ishibuchi，Hisao,et al. An Improved Local Search Method for Large-Scale Hypervolume Subset Selection[J]. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION,2022,PP(99):1-1.

Nan，Yang,Shang，Ke,Ishibuchi，Hisao,&He，Linjun.(2022).An Improved Local Search Method for Large-Scale Hypervolume Subset Selection.IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION,PP(99),1-1.

Nan，Yang,et al."An Improved Local Search Method for Large-Scale Hypervolume Subset Selection".IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION PP.99(2022):1-1.