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

Abstract

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.