摘要
粒子群优化算法(PSO)是合成天线阵列预期方向图的有效手段。但对于某些大型平面阵列方向图复杂的非线性优化问题,该算法收敛速度慢且最优粒子易陷入局部最优解,因而使得算法失效。针对这一问题,该文提出一种改进PSO算法来提高传统PSO算法的收敛特性。该算法在初始化最优粒子时采用解析初值而不是随机初值。对于给定的预期方向图,通过矩阵运算解析对应该方向图的阵元权系数。之后将这些权系数指定为任意一个粒子的解析初值,而种群的其他粒子仍然赋随机初值,之后再衔接标准PSO算法的寻优迭代过程。这种初始化方法使得种群粒子在寻优搜索过程开始之前,即可得到最优粒子初值的有效估计。仿真结果表明,相对于全部粒子赋随机初值的标准算法而言,这种改进算法收敛速度更快,适应度值收敛得更深,因而有效提高了算法的收敛特性,从而能够得到满足预期方向图指标要求的优化结果。
Particle Swarm Optimization （PSO） is an efficient technology to synthesize desired pattern of antenna arrays. But in some complicated cases, the optimizer will fail because the optimum particles fall into local best solutions and the convergence is so bad, especially for nonlinear optimization of large planar arrays pattern. To solve this problem, a modified optimizer is presented to improve the convergence of traditional PSO by means of initializing the particles with analytical values rather than random values. For any given desired pattern, the corresponding aperture weights can be derived by matrix operations and these weights are then used as a particle＇s initial values while other particles are still initialized randomly. By this initialization, an efficient estimation of the optimum particle＇s initial values can be achieved before the beginning of all particles searching process. After that the standard PSO iterations work as usual. The simulation results prove that this modified optimizer converges more rapidly and deeply than the traditional PSO and more satisfying global solutions and desired pattern could be obtained.
出处
《电子与信息学报》
EI
CSCD
北大核心
2017年第10期2340-2345,共6页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61273095)~~
关键词
有源相控阵
平面阵列
副瓣干扰
方向图优化
粒子群优化算法
Active Electronically Scanned Array （AESA）
Planar array
Sidelobes interference
Patternoptimization
Particle Swarm Optimization （PSO）
