Surrogate-assisted large-scale expensive optimization enhanced by local exploitation

Many real-world engineering optimization problems allow only a limited number of objective function evaluations due to the high costs associated with the evaluation procedures. Surrogate-assisted evolutionary algorithms have shown remarkable success in expensive optimization problems but face significant challenges when problem dimensions increase substantially. The curse of dimensionality makes it difficult to train accurate surrogate models for large-scale problems with limited training data. To address this challenge, we propose a surrogate-assisted algorithm that incorporates a local exploitation strategy, referred to as SA-LSEO-LE. A divide-and-conquer approach is employed to decompose the large-scale problem into several low-dimensional sub-problems, enabling more effective surrogate modeling. By sequentially updating a set of sub-populations for different sub-problems using a modified social learning particle swarm optimization algorithm assisted by surrogate models, the algorithm generates offspring solutions for the large-scale expensive problem, thereby enhancing exploration capability. In addition, a local exploitation strategy is introduced to search for better solutions in the vicinity of the best solution found so far. Experimental results on CEC’2013 benchmark problems indicate that the proposed approach significantly outperforms three state-of-the-art algorithms designed for large-scale expensive optimization problems. The effectiveness of the method is further validated on a 1200-dimensional real-world power system optimization problem, confirming its practical applicability.