Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 4977-4984 |
| Number of pages | 8 |
| Journal | IEEE Transactions on Antennas and Propagation |
| Volume | 70 |
| Issue number | 7 |
| Early online date | 1 Mar 2022 |
| DOIs | |
| Publication status | Published - Jul 2022 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 1963-2012 IEEE.
Funding
This work was supported in part by the National Natural Science Foundation of China under Grant 61976111 and Grant 51805180, in part by the Guangdong Provincial Key Laboratory under Grant 2020B121201001, in part by the Program for Guangdong Introducing Innovative and Entrepreneurial Teams under Grant 2017ZT07X386, and in part by the Shenzhen Science and Technology Program under Grant KQTD2016112514355531 and Grant JCYJ20180504165652917.
Keywords
- Antenna synthesis
- machine learning (ML)
- particle swarm optimization (PSO)
- surrogate assisted evolutionary algorithm (SAEA)
- surrogate prescreening