摘要
天线的联合仿真是天线参数优化的一种有效手段。针对经典平面贴片八木天线设计中的参数优化,介绍了Python-HFSS联合仿真方法在天线设计中的应用及优势。基于传统粒子群(Particle Swarm Optimization,PSO)算法,详细阐述了联合仿真具体的实施步骤和逻辑实现,包括HFSS软件中脚本的录制、Python中对录制脚本的使用、PSO算法在优化天线时的算法逻辑以及改进点建议。进一步提出了一种改进型的适应度值预测粒子群(Fitness Estimate Particle Swarm Optimization,FEPSO)算法,并将其运用在天线优化当中。仿真结果表明,所提改进算法在精度相差不大的情况下,比传统的PSO算法减少了近一半在HFSS软件中的仿真次数,整体效率提升了40%以上。此联合仿真具有较好的应用价值,可以推广到其他类型天线的设计与优化当中。
Antenna co-simulation is an effective method for antenna parameter optimization.The application and advantages of Python-HFSS co-simulation method in antenna design are demonstrated by taking the parameter optimization of classical planar patch Yagi antenna design as an example.Based on the traditional Particle Swarm Optimization(PSO)algorithm,the logical implementation and the corresponding specific implementation steps of the co-simulation method are described in details,including the recording of scripts in HFSS software,the use of recording scripts in Python,the algorithm logic of particle swarm optimization algorithm in antenna optimization and suggestions for improvement.Furthermore,an improved Fitness Estimate Particle Swarm Optimization(FEPSO)is proposed to optimize the antenna.Simulation results show that the improved algorithm reduces nearly half of the simulation times in HFSS software compared with the traditional standard PSO algorithm,and improves the overall efficiency by more than 40%.This co-simulation has promising application potential and could be extended to the design and optimization of other types of antennas.
作者
崔若愚
牛调明
梅中磊
CUI Ruoyu;NIU Tiaoming;MEI Zhonglei(School of Information Science and Engineering,Lanzhou University,Lanzhou 730000,China)
出处
《无线电工程》
北大核心
2022年第10期1879-1886,共8页
Radio Engineering
基金
国家自然科学基金(61701207,61631007,61640017)
国家重点研发计划(2019Y-FA0405403)
甘肃省自然科学基金(20JR10RA604)
东南大学毫米波国家重点实验室基金项目(k201717)。
