基于双变异策略差分进化算法的稀布线阵优化方法  

Sparse Linear Array Optimization Based on Differential Evolution Algorithm with Dual Mutation Strategies

作　　者：王莉 WANG Li(Rocket Force University of Engineering,Xi’an 710025,China)

机构地区：[1]火箭军工程大学,陕西西安710025

出　　处：《火箭军工程大学学报》2025年第1期50-59,共10页Journal of Rocket Force University of Engineering

基　　金：国家自然科学基金资助项目(62201606)。

摘　　要：针对稀布线阵天线优化中如何有效降低峰值旁瓣电平的问题,提出了一种基于柯西-高斯双变异策略的改进差分进化算法。该算法通过Tent混沌映射生成初始值,增加了种群多样性;随迭代和适应值变化,对缩放因子进行动态调整,有助于算法更好地进行寻优;当算法出现早熟迹象时,采用双变异策略助力跳出局部最优,实现全局最优解搜索。通过与经典算法进行对比,利用标准函数测试验证了算法的优化性能。通过3种天线实例的仿真实验结果表明:所提改进算法能够有效降低峰值旁瓣电平,提升天线效能,与对比算法中的最优算法相比较,所提算法50次实验的最优峰值旁瓣电平分别降低了0.11 dB、0.62 dB和2.42 dB,平均峰值旁瓣电平分别降低了0.04 dB、0.28 dB和1.5 dB。To effectively reduce the peak sidelobe level in a sparse linear array antenna,an improved differential evolution algorithm based on a dual mutation strategy combining Cauchy and Gaussian mutations was proposed.The initial value of the proposed algorithm was generated through Tent chaotic mapping to increase the population diversity.Simultaneously,it was dynamically adjusted using iterations and fitness values,which improved the optimization of the algorithm.When the algorithm showed signs of prematurity,a dual mutation strategy was introduced to escape from local optima and achieve a global optimal solution search.By comparing with those of classical algorithms,the optimized performance of the proposed algorithm was verified through standard function tests.Results of simulation experiments on three antenna examples showed that the proposed algorithm can effectively reduce the peak sidelobe level and enhance antenna efficiency.Compared with the peak sidelobe levels of the optimal algorithm among the comparison algorithms,the optimal peak sidelobe levels of the proposed algorithm in 50 experiments are reduced by 0.11 dB,0.62 dB,and 2.42 dB respectively,while the average peak sidelobe levels are reduced by 0.04dB,0.28dB,and 1.5dB respectively.

关 键 词：阵列天线 峰值旁瓣电平 差分进化算法 双变异策略 

分 类 号：TN820[电子电信—信息与通信工程]