LTR-HSS: A Learning-to-Rank Based Framework for Hypervolume Subset Selection

Cheng Gong, Ping Guo, Tianye Shu, Qingfu Zhang, Hisao Ishibuchi

Abstract: Hypervolume subset selection (HSS) plays an important role in various aspects of the field of evolutionary multi-objective optimization, such as environmental selection and post-processing for decision-making. The goal of these problems is to find the optimal subset that maximizes the hypervolume from a given candidate solution set. Many methods have been developed to solve or approximately solve different types of HSS problems. However, existing approaches cannot effectively solve HSS problems with a large number of objectives within a short computation time. This drawback directly limits their applicability as a component for developing new EMO algorithms. In this paper, we propose a novel learning-to-rank based framework, named LTR-HSS, for solving the challenging HSS problems with a large number of objectives. The experimental results show that, compared to other state-of-the-art HSS methods, our proposed LTR-HSS requires a shorter computation time to solve HSS problems with large numbers of objectives while achieving superior or competitive hypervolume performance. This demonstrates the potential of our method to be integrated into algorithms for many-objective optimization.