High-Dimensional Expensive Multiobjective Optimization by Surrogate-assisted Multifactorial Evolutionary Algorithm

Abstract

Surrogate-assisted evolutionary algorithms (SAEAs) are useful optimizers for solving expensive multiobjective optimization problems (EMOPs). Recent works have shown that decomposition-based SAEAs perform well for medium-dimensional EMOPs, i.e., 30-100 dimensional problems. However, they evaluate a solution in each subproblem, making it hard to solve high-dimensional problems, i.e., 100 or more dimensional problems, because the search efficiency is reduced. To deal with this challenge, we employ a multifactorial evolutionary algorithm into a decomposition-based SAEA for solving similar subproblems simultaneously. Experimental results show that our proposed algorithm outperforms some state-of-the-art SAEAs with up to 300 dimensional EMOPs.