Investigating the Effectiveness of Surrogate Models in High Dimentional Expensive Multi-Objective Optimization Problems

Abstract

Surrogate-assisted multi-objective evolutionary algorithms (SAMOEAs) are effective ap-(breakpoint)proaches for solving expensive multi-objective optimization problems (EMOPs). However, when the search space of EMOPs becomes high-dimensional, the effectiveness of SAMOEAs significantly degrades. In this work, we investigate how much such performance degradation will occur by comparing SAMOEAs with vanilla evolutionary algorithms. Experimental results show that, surprisingly, SAMOEAs tend to suffer from outperforming vanilla evolutionary algorithms.