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

Yang Nan, Ke Shang, Hisao Ishibuchi, Linjun He

Abstract: 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.