An Efficient Surrogate Assisted Particle Swarm Optimization for Antenna Synthesis

被引：33

作者：

Fu, Kai ^{[1
]}

Cai, Xiwen ^{[1
]}

Yuan, Bo ^{[1
]}

Yang, Yang ^{[2
]}

Yao, Xin ^{[1
,3
]}

机构：

[1] Southern Univ Sci & Technol, Dept Comp Sci & Engn, Guangdong Prov Key Lab Brain Inspired Intelligent, Shenzhen 518055, Peoples R China

[2] Univ Technol Sydney, Sch Elect & Data Engn, Ultimo, NSW 2007, Australia

[3] Univ Birmingham, CERCIA, Sch Comp Sci, Birmingham B15 2TT, W Midlands, England

来源：

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION | 2022年 / 70卷 / 07期

基金：

中国国家自然科学基金;

关键词：

Antennas; Optimization; Computational modeling; Predictive models; Particle swarm optimization; Training; Prediction algorithms; Antenna synthesis; machine learning (ML); particle swarm optimization (PSO); surrogate assisted evolutionary algorithm (SAEA); surrogate prescreening; GLOBAL OPTIMIZATION; DESIGN; MODELS;

D O I：

10.1109/TAP.2022.3153080

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：4977 / 4984

页数：8

共 36 条

[1] Space mapping: The state of the art [J].