An Improved Local Search Method for Large-Scale Hypervolume Subset Selection
提出一种新的局部搜索方法及其扩展版本，用于从大规模候选解集中快速选出超体积最大的子集，实验证明比现有方法更高效。
Hypervolume subset selection (HSS) has received considerable attention in the field of evolutionary multiobjective optimization (EMO). It aims to select a representative subset from a candidate solution set so that the hypervolume (HV) 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 HV 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 article, 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. The 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.