Surrogate-Assisted Multipopulation Particle Swarm Optimizer for High-Dimensional Expensive Optimization

Surrogate-assisted evolutionary algorithms (SAEAs) are well suited for computationally expensive optimization. However, most existing SAEAs only focus on low- or medium-dimensional expensive optimization. Thus, a novel SAEA for high-dimensional expensive optimization, denoted as surrogate-assisted multipopulation particle swarm optimizer (SA-MPSO), is proposed and fully investigated in this work. The proposed algorithm employs a parameter-free clustering technique, denoted as affinity propagation clustering, to generate several subswarms. A surrogate-assisted learning strategy-based particle swarm optimizer is proposed for guiding the search of each subswarm. Furthermore, a model management strategy is adapted to choose the promising particles for real fitness evaluations. Finally, a subswarm diversity maintenance scheme and a surrogate-based trust region local search technique are introduced to enhance both exploration and exploitation. The experimental results on commonly used benchmark test problems with dimensions varying from 30 to 100 and airfoil design problem have shown that SA-MPSO outperforms some state-of-the-art methods.

Automated summary

This paper proposes a novel surrogate-assisted evolutionary algorithm (SAEA) for high-dimensional expensive optimization. The algorithm uses affinity propagation clustering to generate multiple subswarms and employs a surrogate-assisted learning strategy to guide the search. Techniques such as model management, subswarm diversity maintenance, and surrogate-based local search are introduced to enhance exploration and exploitation. Experimental results demonstrate the superiority of the proposed algorithm over state-of-the-art methods.