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Efficient AI-Driven Design of Microwave Antennas Using PSADEA

机译：使用PSADEA的微波天线高效的AI驱动设计

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摘要

Nowadays, artificial intelligence plays a very significant role in the optimal design of microwave devices such as antennas. In particular, antenna design automation via surrogate model-based optimization (SMBO) methods is attracting a lot of interest due to the efficiency improvement in terms of computational cost. The parallel surrogate model-assisted hybrid differential evolution for antenna optimisation (PSADEA) method is a state-of-the-art SMBO method. In this paper, PSADEA is used to synthesize a compact slotted monopole antenna for ultra wide band body-centric applications. The performance of PSADEA is compared with 2019 Computer Simulation Technology - Microwave Studio (CST-MWS) optimizers: trust region framework (TRF) and particle swarm optimisation (PSO). Results from the comparisons show that PSADEA obtains very satisfactory design solutions for the monopole antenna in all runs using an affordable optimization time in each, whereas the alternative optimizers fail to obtain a satisfactory design solution in all runs. A close agreement between the simulated and measured results for the fabricated prototype of a typical PSADEA synthesized design for the monopole antenna validates the design solution quality of PSADEA.
机译：如今，人工智能在诸如天线的微波器件的最佳设计中起着非常重要的作用。特别是，由于基于代理模型的优化（SMBO）方法，天线设计自动化是由于计算成本方面的效率提高而吸引了很多兴趣。天线优化（PSADEA）方法的并行代理模型辅助混合差分演进是一种最先进的SMBO方法。在本文中，PSADEA用于合成用于超宽频带身体应用的紧凑型开槽单极天线。 PSADEA的性能与2019年计算机仿真技术进行比较 - 微波工作室（CST-MWS）优化器：信任区域框架（TRF）和粒子群优化（PSO）。比较结果表明，PSADEA在所有运行中使用每个运行的单极天线的设计解决方案非常令人满意，而替代优化器不能在所有运行中获得令人满意的设计解决方案。为单极天线典型PSADEA合成设计的制造原型之间的模拟和测量结果之间的密切一致验证了PSADEA的设计溶液质量。

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