A Surrogate-Assisted Multiswarm Optimization Algorithm for High-Dimensional Computationally Expensive Problems

This article presents a surrogate-assisted multiswarm optimization (SAMSO) algorithm for high-dimensional computationally expensive problems. The proposed algorithm includes two swarms: the first one uses the learner phase of teaching-learning-based optimization (TLBO) to enhance exploration and the second one uses the particle swarm optimization (PSO) for faster convergence. These two swarms can learn from each other. A dynamic swarm size adjustment scheme is proposed to control the evolutionary progress. Two coordinate systems are used to generate promising positions for the PSO in order to further enhance its search efficiency on different function landscapes. Moreover, a novel prescreening criterion is proposed to select promising individuals for exact function evaluations. Several commonly used benchmark functions with their dimensions varying from 30 to 200 are adopted to evaluate the proposed algorithm. The experimental results demonstrate the superiority of the proposed algorithm over three state-of-the-art algorithms.

Automated summary

This article presents a surrogate-assisted multiswarm optimization algorithm for high-dimensional computationally expensive problems. The proposed algorithm consists of two swarms that enhance exploration and convergence using teaching-learning-based optimization and particle swarm optimization techniques. Additional strategies such as dynamic swarm size adjustment, coordinate systems, and prescreening criterion contribute to the algorithm's superior performance in comparison to three state-of-the-art algorithms.