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. © 2019, The Editorial Board of Journal of Xidian University. All right reserved.