An Efficient Surrogate Assisted Particle Swarm Optimization for Antenna Synthesis
An Efficient Surrogate Assisted Particle Swarm Optimization for Antenna Synthesis
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DOI：
10.1109/tap.2022.3153080
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发表时间：
2022-07-01
影响因子：
5.7
通讯作者：
Yao, Xin
中科院分区：
文献类型：
--
作者：
Fu, Kai;Cai, Xiwen;Yao, Xin
利用机器学习方法的预测能力，机器学习辅助进化算法已被视为天线设计自动化的有效解决方案。本文提出了一种高效的基于ml的代理辅助粒子群优化算法。该算法将粒子群算法与两种基于ml的近似模型紧密结合。然后，提出了一种新的混合预筛选（mixP）策略，用于全波电磁（EM）模拟中挑选有前途的个体。随着优化过程的进行，一旦获得新的训练数据，机器学习模型就会动态更新。最后，通过三个实际天线实例验证了算法的有效性。结果表明，与其他方法相比，SAPSO-mixP方法可以在较少的EM模拟次数下获得较好的结果。
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.