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An Efficient Method for Complex Antenna Design Based on a Self Adaptive Surrogate Model-Assisted Optimization Technique
IEEE Transactions on Antennas and Propagation ( IF 5.7 ) Pub Date : 2021-01-18 , DOI: 10.1109/tap.2021.3051034
Bo Liu 1 , Mobayode O. Akinsolu 2 , Chaoyun Song 3 , Qiang Hua 4 , Peter Excell 2 , Qian Xu 5 , Yi Huang 4 , Muhammad Ali Imran 1
Affiliation  

Surrogate models are widely used in antenna design for optimization efficiency improvement. Currently, the targeted antennas often have a small number of design variables and specifications, and the surrogate model training time is short. However, modern antennas become increasingly complex, which needs much more design variables and specifications, making the training time become a new bottleneck, i.e., in some cases, even longer than electromagnetic (EM) simulation time. Therefore, a new method, called training cost reduced surrogate model-assisted hybrid differential evolution for complex antenna optimization (TR-SADEA), is presented in this article. The key innovations include: 1) a self-adaptive Gaussian process surrogate modeling method with a significantly reduced training time while mostly maintaining the antenna performance prediction accuracy and 2) a new hybrid surrogate model-assisted antenna optimization framework that reduces the training time and increases the convergence speed. An indoor base station antenna with 2G to 5G cellular bands (45 design variables and 12 specifications) and a 5G outdoor base station antenna (23 design variables and 18 specifications) are used to demonstrate TR-SADEA. Experimental results show that more than 90% of the training time and about 20% iterations (simulations and surrogate modeling) are reduced compared to a state-of-the-art method while obtaining high antenna performance.

中文翻译:

基于自适应替代模型辅助优化技术的复杂天线设计有效方法

代理模型广泛用于天线设计中,以提高效率。当前,目标天线通常具有少量的设计变量和规格,并且替代模型训练时间短。但是,现代天线变得越来越复杂,需要更多的设计变量和规格,使得训练时间成为新的瓶颈,即在某些情况下甚至比电磁(EM)仿真时间还要长。因此,本文提出了一种新的方法,称为用于复杂天线优化的训练成本降低的替代模型辅助混合差分进化(TR-SADEA)。关键创新包括:1)一种自适应的高斯过程替代建模方法,可显着减少训练时间,同时主要保持天线性能的预测精度; 2)一种新的混合替代模型辅助天线优化框架,可减少训练时间并提高收敛速度。具有2G至5G蜂窝频段的室内基站天线(45个设计变量和12个规格)和5G室外基站天线(23个设计变量和18个规格)用于演示TR-SADEA。实验结果表明,与现有技术相比,该方法减少了90%以上的训练时间,并减少了约20%的迭代(模拟和替代建模),同时获得了较高的天线性能。
更新日期:2021-01-18
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