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. Two different pre-screening criteria based on the corresponding evolutionary rules are proposed to select promising individuals for exact function evaluations. The second swarm uses the particle swarm optimization in the later stage to accelerate convergence. The first swarm uses the teaching-learning-based optimization in the early stage to enhance the exploration.
Two swarms are, respectively, used in different optimization states.
In this paper, a surrogate-assisted hybrid swarm optimization algorithm is proposed to solve high-dimensional computationally expensive problems.
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