摘要
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。
For the optimization of spare linear array,a new method based on the improved adaptive particle swarm optimization（PSO） is proposed.Firstly,the modified PSO algorithm adjusts its inertia weight and learning factors adaptively via the adaptive policy according to the fitness of the particle,which improves the searching ability of the population,and then the velocity expression is modified,which guarantees the update of the velocity.To further accelerate the convergence rate,a crossover strategy is introduced when the algorithm is at a state of stagnation.This method completes the synthesis of the spare linear array with multi-constraint efficiently,and achieves lower peak sidelobe level（PSLL）;numerical simulation shows the effectiveness of the algorithm.
出处
《微波学报》
CSCD
北大核心
2011年第5期32-35,68,共5页
Journal of Microwaves
基金
国防科技重点实验室基金(9140C1004070904)
关键词
粒子群算法
交叉策略
稀布阵
旁瓣电平
particle swarm optimization
crossover strategy
spare array
sidelobe level
