基于改进免疫算法与HFSS联合仿真的天线多目标优化方法研究

    Research on Antenna Multi-objective Optimization Method Based on Improved Immune Algorithm and HFSS Co-simulation

    • 摘要: 由于HFSS电磁仿真软件在天线结构参数优化过程中存在优化效果不显著、效率低的问题,文中提出了一种基于改进免疫算法与HFSS联合仿真的天线多目标优化方法。针对一款末端加载的弯折偶极子天线,提出了中心频点与目标频点的偏移小、低回波损耗、阻抗匹配和高增益的四个目标函数,并从SPM混沌映射生成初始化种群、标准化欧氏距离计算亲和度、自适应重组和变异概率对免疫算法进行改进,利用MATLAB-HFSS-API实现联合仿真,进行天线结构参数多目标寻优。试验表明,改进免疫算法有较强的寻优能力,得到的标签天线结构参数优化结果能够更大程度满足优化目标需求,优化耗时仅为HFSS电磁仿真软件的32.5%,天线优化设计效率提升明显。

       

      Abstract: Due to the issues of insignificant optimization effects and low efficiency of HFSS electromagnetic simulation software in the process of antenna structure parameter optimization, this paper proposes an antenna multi-objective optimization method based on improved immune algorithm and HFSS joint simulation. For an end-loaded bent dipole antenna, four objective functions are proposed: small offset between the center frequency and the target frequency, low return loss, impedance matching, and high gain. The immune algorithm is improved by generating the initial population from SPM chaotic map, calculating affinity by standardized Euclidean distance, and adopting adaptive recombination and mutation probability. MATLAB-HFSS-API is used to achieve joint simulation and multi-objective optimization of antenna structure parameters. The example verification shows that the improved immune algorithm has strong optimization capabilities, and the optimization results of the tag antenna structure parameters obtained can meet the optimization target requirements to a greater extent. The optimization time is only 32.5% of the HFSS electromagnetic simulation software, and the antenna optimization design efficiency is significantly improved.

       

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