基于改进布谷鸟算法的稀布线阵方向图优化  

Sparsely⁃distributed linear array pattern optimizationbased on modified chaotic cuckoo search algorithm

作　　者：向阳雨 贾维敏 张峰干 XIANG Yangyu;JIA Weimin;ZHANG Fenggan(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：《现代电子技术》2021年第13期7-12,共6页Modern Electronics Technique

基　　金：国家自然科学基金(61601474)资助课题;国家自然科学基金(61501469)资助课题。

摘　　要：降低峰值旁瓣电平对提高阵列天线性能有重要意义,采用传统智能算法易出现搜索精度不高、陷入局部最优等问题。为了提高优化效率,提出一种基于新的更新策略的混沌布谷鸟搜索算法。该算法首先使用混沌映射Sinusoidal代替固定步长因子,利用混沌算子的无序和遍历性提高算法的全局搜索能力;其次,分别针对上一代的最优种群、非最优种群和单个个体提出不同更新策略,使种群跳出了固有的更新模式,避免算法过早进入收敛。在稀布线阵仿真实验中,通过对比4种传统优化算法,该算法可以更有效地抑制线阵方向图峰值旁瓣电平,使优化效果得到了显著提升。The traditional intelligent algorithms have lower search accuracy and are prone to getting into the local optimum.In view of this,a modified chaotic cuckoo search(MCCS)algorithm based on the new update strategy is proposed to improve the optimizing efficiency.In the algorithm,chaotic mapping Sinusoidal is used to replace the fixed step factors first,and then the randomness and ergodicity of the chaos operator are used to improve the global searching ability of the algorithm.Furthermore,different update strategies for the optimal population,the non⁃optimal population and the individuals of the last generation are provided,which makes the population not limited to the routine updating mode,so as to avoid premature convergence of the algorithm.In the simulation experiment of the sparsely⁃distributed linear array,the MCCS algorithm can more effectively suppress the peak sidelobe level(PSLL)of the linear array pattern and enhance the optimization effect significantly in comparison with the four traditional optimization algorithms.

关 键 词：稀布线阵 峰值旁瓣电平 布谷鸟搜索算法 混沌映射 天线方向图 低副瓣 智能算法 优化效率 

分 类 号：TN820.12-34[电子电信—信息与通信工程]