An Efficient Surrogate Assisted Particle Swarm Optimization for Antenna Synthesis

By virtue of the prediction abilities of machine learning (ML) methods, the ML-assisted evolutionary algorithm has been treated as an efficient solution for antenna design automation. This article presents an efficient ML-based surrogate-assisted particle swarm optimization (SAPSO). The proposed algorithm closely combines the particle swarm optimization (PSO) with two ML-based approximation models. Then, a novel mixed prescreening (mixP) strategy is proposed to pick out promising individuals for full-wave electromagnetic (EM) simulations. As the optimization procedure progresses, the ML models are dynamically updated once new training data are obtained. Finally, the proposed algorithm is verified by three real-world antenna examples. The results show that the proposed SAPSO-mixP can find favorable results with a much smaller number of EM simulations than other methods.

Automated summary

This article proposes an efficient ML-based surrogate-assisted particle swarm optimization algorithm for antenna design automation. The algorithm combines particle swarm optimization with two ML-based approximation models and introduces a novel prescreening strategy to select promising antenna designs for simulation. The results show that the proposed algorithm can find favorable results with fewer simulations compared to other methods.