To address the issue of traditional Particle Swarm Optimization (PSO) being prone to local optima and insufficient global search capability in sparse phased array optimization, a hybrid optimization algorithm integrating PSO with a Genetic Algorithm (GA) is proposed. Within the PSO framework, the proposed algorithm incorporates the adaptive crossover and mutation operations of the GA to enhance population diversity. It combines an adaptive weighting factor and a constriction factor to balance global exploration and local exploitation capabilities. Furthermore, a density-weighted method is employed to generate a high-quality initial population, thereby accelerating convergence. The proposed algorithm is applied to an 8 × 8 planar sparse array. On the E-plane (φ = 0°) and H-plane (φ = 90°), simulation results indicate that the achieved normalized maximum sidelobe level is −23.14 dB, which is significantly superior to those obtained by standalone PSO and GA. Based on these simulation results, microstrip patch antennas are introduced for array constitution and analysis. Full-wave electromagnetic simulation proves that the proposed sparse array has the ability of wide-angle scanning and low sidelobe. Our work demonstrates that the PSO-GA hybrid algorithm effectively enhances search capability and convergence performance, providing a reliable solution for sparse array design.
Li et al. (2026) studied this question.