Improved Particle Swarm Optimization in Synthesis of Rectangular Thinned Array Antennas

The particle swarm optimization (PSO) algorithm based on the comprehensive learning (CL) strategy is proposed and applied to address array synthesis of thinned array. Firstly, CL strategy is introduced to the binary particle swarm optimization (BPSO) algorithm and the probability mapping function in original BPSO algorithm is also modified to increase population diversity and global search capability and improve the convergence speed of array position optimization. Subsequently, the comprehensive learning particle swarm optimization (CLPSO) algorithm is utilized to optimize the amplitude and phase of the excitation current of the thinned array, and effectively prevented premature convergence and further reduced the peak sidelobe level of antennas. Results demonstrate that, compared to the traditional algorithm, the proposed methods exhibit superior global search capability and local convergence, achieve a lower PSLL level and provide a novel and efficient solution for the thinned array optimization design problem.