High-Dimensional Expensive Multiobjective Optimization Using a Surrogate-Assisted Multifactorial Evolutionary Algorithm
Yuma Horaguchi, Masaya Nakata · Proceedings of the Genetic and Evolutionary Computation Conference · 2025
The performance of surrogate-assisted multiobjective evolutionary algorithms (SAMOEAs) often degrades in high-dimensional problems. Recent studies have shown that decomposition-based approaches are particularly effective in handling high-dimensional search spaces, owing to their problem-simplifying capability. However, existing decomposition-based SAMOEAs are designed to sequentially solve each decomposed subproblem, still unnecessarily consuming function evaluations (FEs) and thus degrading the search efficiency. To address this issue, this paper proposes a novel decomposition-based SAMOEA that employs a multifactorial evolutionary algorithm (MFEA). The proposed algorithm aggregates multiple subproblems randomly and it collectively solves them using a surrogate-assisted MFEA framework. This approach enables the efficient discovery of promising solutions across multiple subproblems in a single FE, enhancing the search efficiency under a limited budget of FEs. Experimental results show that our proposed algorithm outperforms state-of-the-art SAMOEAs on problems with up to 300 dimensions. This suggests that our surrogate-assisted MFEA framework can bring out the further potential of decomposition-based SAMOEAs.