abstract
- The hypervolume indicator (HV) is widely used in evolutionary multi-objective optimization for performance evaluation and algorithm design. However, its utility heavily depends on selecting a reference point (RP). Depending on this point, HV may prefer non-uniform Pareto front approximations (PFAs) over other more uniform distributions. While existing studies have explored the impact of RP specification regarding ¿-distribution settings, its impact on greedy inclusion hypervolume-based subset selection (GI-HSS) algorithms remains underexamined. These algorithms rely on incremental individual contributions to HV. This paper investigates the effect of the RP specification on the uniformity of PFAs generated by a GI-HSS algorithm. The focus is on two-objective Pareto fronts characterized by linear, concave, convex, and disconnected geometries. Using the lazy GI-HSS algorithm as a framework, we evaluate a comprehensive range of RP settings to identify those that promote more uniform distributions with up to 210 points. Our findings provide new insights into RP selection and offer practical guidelines for enhancing the performance of GI-HSS algorithms in multi-objective applications. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.