A Surrogate-Assisted Clustering-Based Evolutionary Algorithm for Expensive Optimization

Surrogate-assisted evolutionary algorithms (SAEAs) are widely used in solving computationally expensive optimization problems. The intricate nature of real-world optimization problems necessitates the development of more efficient SAEAs to effectively address these challenges. In this paper, we propose a novel algorithm, named surrogate-assisted clustering-Based evolutionary algorithm (SACBEA) for solving expensive optimization problems. SACBEA is an innovative combination of dynamic surrogate-assisted particle swarm optimization (DSAP) and clustering-based local search (CBLS) for the balance between exploration and exploitation. Specifically, the DSAP employs the top-ranked solutions in the initialization of population, using Particle Swarm Optimization (PSO) as the evolutionary operator. Concurrently, a dynamic surrogate model is utilized for fitness prediction. In addition, the CBLS employs the k-means clustering algorithm to identify the promising region, initializing the population within this area for a more focused search. And the Differential Evolution (DE) algorithm is integrated into CBLS. SACBEA switches between DSAP and CBLS based on search performance. When one search mechanism fails to find a better solution, SACBEA seamlessly transitions to the other. To verify the effectiveness of SACBEA, a test set of 13 benchmark problems is adopted to conduct comparative experiments. Experimental results demonstrate the significant advantages of SACBEA over several state-of-the-art algorithms.