An Efficient Surrogate Assisted Particle Swarm Optimization for Antenna Synthesis

Kai FU, Xiwen CAI, Bo YUAN, Yang YANG, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

19 Citations (Scopus)


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. © 1963-2012 IEEE.
Original languageEnglish
Pages (from-to)4977-4984
Number of pages8
JournalIEEE Transactions on Antennas and Propagation
Issue number7
Early online date1 Mar 2022
Publication statusPublished - Jul 2022
Externally publishedYes


  • Antenna synthesis
  • machine learning (ML)
  • particle swarm optimization (PSO)
  • surrogate assisted evolutionary algorithm (SAEA)
  • surrogate prescreening


Dive into the research topics of 'An Efficient Surrogate Assisted Particle Swarm Optimization for Antenna Synthesis'. Together they form a unique fingerprint.

Cite this