Efficient Large-Scale Expensive Optimization via Surrogate-Assisted Subproblem Selection

Traditional large-scale evolutionary algorithms are limited in their ability to solve certain real-world applications with high-dimensional, closed-box, and computationally expensive objectives due to their need for numerous objective evaluations. Surrogate-assisted evolutionary algorithms (SAEAs) have shown effective for expensive closed-box optimization by relying on inexpensive surrogate models. However, large-scale optimization remains challenging for SAEAs due to the exponentially growing search space and the presence of multiple local optima, resulting in difficulty in training a proper model due to the lack of samples. To address these challenges, we propose constructing an initial surrogate model on randomly selected dimensions and calculating a Gaussian distribution for each sampled dimension. The surrogate then provides predictions when perturbing each sampled dimension by sampling from the distribution, enabling the identification of the most important variables for constructing an active subproblem to reduce the search space. A secondary surrogate model, built for the active subproblem, guides the offspring generation and environmental selection for a modified particle swarm optimization algorithm to effectively explores the subspace while escaping local optima in large-scale problems. Experimental results on CEC’2013 and CEC’2010 benchmark problems show that the proposed method outperforms state-of-the-art algorithms in addressing large-scale expensive optimization problems. The efficiency of the proposed method is further verified on CEC’2010 benchmark problems extended to 2000 dimensions.