摘要
针对目前粒子群优化算法在多零点低旁瓣约束的阵列天线方向图综合中早熟收敛、易陷入局部极值的问题,提出了一种改进的粒子群优化算法MSPSO,在多子群、层次化的模型中采用von Neumann邻域结构,以改善收敛速度和优化精度。建立一种新的目标函数模型,对顶层和底层的子群分别采用适合其特点的适应值目标函数,平衡了算法的全局和局部搜索能力。仿真结果表明,将该算法应用于阵列天线方向图综合中,取得了很好的优化效果。
According to the prematurity and easily trapping in local optimum by using particle swarm optimization （PSO） in the pattern synthesis of antenna arrays with sidelobe reduction and nulls control, a multiple subpopulation PSO algorithm （MSPSO）is presented in this paper. The MSPSO algorithm is built by employing the strategy of hierarchy and subpopulation with neighbor structure of von Neumann to improve the convergence speed and accuracy. A modified objective function model that adopts different fitness functions according to the character of the top and bottom layer is proposed to balance effectively the local and global searching ability of MSPSO algorithm. The simulation results show that the MSPSO algorithm can achieve relatively high performance in the pattern synthesis of antenna arrays.
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2011年第2期237-241,共5页
Journal of University of Electronic Science and Technology of China
基金
国家自然科学基金重大项目(60990320
60990323)
国家863计划(2009AA01Z230)
关键词
阵列天线
进化算法
层次化
粒子群优化算法
antenna arrays
evolutionary algorithms
hierarchy
particle swarm optimization
