A surrogate-assisted hybrid swarm optimization algorithm for high-dimensional computationally expensive problems

In this paper, a surrogate-assisted hybrid swarm optimization algorithm is proposed to solve high-dimensional computationally expensive problems. Two swarms are, respectively, used in different optimization states. The first swarm uses the teaching-learning-based optimization in the early stage to enhance the exploration. The second swarm uses the particle swarm optimization in the later stage to accelerate convergence. Two different pre-screening criteria based on the corresponding evolutionary rules are proposed to select promising individuals for exact function evaluations. Several commonly used benchmark functions with their dimensions varying from 30 to 200 and an engineering optimization problem are used to validate the efficiency of the proposed algorithm. In addition, a comprehensive analysis is conducted to demonstrate the effectiveness of each main component of the proposed algorithm. The experimental results demonstrate the superiority of the proposed algorithm over other compared algorithms.