针对稀布线阵的阵元位置优化问题,提出了一种基于改进的自适应粒子群算法的稀布阵综合新方法。该方法首先采用自适应策略,根据粒子的适应度值自适应地调整其惯性权重和学习因子,提高了种群的寻优能力;然后对粒子群算法的速度更新公式进行了修正,保证了速度的有效更新;在算法停滞时,通过引入交叉策略进一步加快了算法的收敛速度。该方法高效地实现了多约束稀布线阵的综合,获得了更低的峰值旁瓣电平,数值仿真验证了算法的有效性。 Aiming at the array element location optimization of sparse linear array, a new method of sparse array synthesis based on improved adaptive particle swarm optimization is proposed. Firstly, this method adaptively adjusts its inertia weight and learning factor according to the fitness value of particle to improve the ability of searching for population. Secondly, the speed update formula of particle swarm optimization algorithm is modified to ensure that the speed Which can be effectively updated. When the algorithm stagnates, the convergence speed of the algorithm is further accelerated by introducing the crossover strategy. The method achieves the synthesis of multiple constrained sparse linear arrays efficiently and achieves lower peak sidelobe levels. The numerical simulation verifies the effectiveness of the proposed algorithm.