改进布谷鸟算法用于阵列天线方向图优化

针对目前群智能优化及进化计算算法在解决直线形阵列天线辐射方向图的旁瓣电平抑制和零陷控制问题时易出现求解精度不高及收敛速度较慢的问题,提出了一种基于扩散变异策略的布谷鸟搜索算法。该算法首先利用聚集扩散策略来提高算法的全局搜索性能,然后引入遗传算法中的基因突变策略,用以提高种群的多样性,进而提高算法的局部搜索能力。仿真结果表明,所提算法在抑制直线形阵列天线方向图的旁瓣电平和控制零陷方面与萤火虫算法、粒子群算法、传统布谷鸟搜索算法、金斑蝶优化算法和蚯蚓优化算法相比,具有更快的收敛速度和更高的求解精度。

Improved cuckoo search algorithm for optimizing the beam patterns of linear antenna arrays

To solve the problems of sidelobe level (SLL) suppression and nulls control of the linear antenna arrays (LAA), a spread variation cuckoo search (SVCS) algorithm is proposed. First, the SVCS uses the aggregated diffusion strategy to improve the possibility of obtaining the global optimal solutions of the algorithm. Second, the gene mutation method of the genetic algorithm is introduced to improve the population diversity so as to avoid the algorithm falling into the local optima. Simulation results show that the proposed SVCS has a better performance in terms of the convergence rate and accuracy compared with the firefly algorithm, the particle swarm optimization, the conventional cuckoo search algorithm, the monarch butterfly optimization algorithm and the earthworm optimization algorithm for reducing the SLL of the LAA. Moreover, the SVCS also has a better performance for solving the joint optimization problem of SLL suppression and nulls control compared with the above mentioned benchmark algorithms.