共查询到17条相似文献,搜索用时 78 毫秒
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粒子群优化算法(PSO)是一种基于群智能的随机优化算法,其理论简单,参数少,易于实现,可用于解决大量非线性、不可微和多峰值的复杂问题。本文介绍了粒子群算法的基本原理和基本流程,研究了如何将这种方法应用于阵列天线的方向图综合上,给出了PSO 算法在阵列天线方向图综合的应用实例,结果表明粒子群算法在阵列天线方向图综合上有很好的应用前景。 相似文献
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基于停滞检测粒子群算法的阵列天线方向图综合 总被引:1,自引:0,他引:1
在线性递减权重粒子群算法的基础上提出了一种改进的粒子群优化算法.新算法采用了合适的邻域结构,通过停滞检测以及对全局最佳粒子的微扰改善了算法的优化速度和收敛特性.仿真结果表明:将此算法应用于天线方向图综合中,在多零点和低旁瓣约束情况下可以取得良好的优化效果. 相似文献
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为了克服粒子群优化算法早熟收敛,本文提出了一种改进的小波变异粒子群优化算法,由于该算法每次迭代时以一定的概率选中粒子进行小波变异扰动,能够克服算法后期易发生早熟收敛和陷入局部最优的缺点。同时将改进的算法应用于天线阵列方向图综合问题中,综合效果好于现有文献。 相似文献
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Synthesis of Thinned Multiple Concentric Circular Ring Array Antennas using Particle Swarm Optimization 总被引:1,自引:0,他引:1
N. Pathak P. Nanda G. K. Mahanti 《Journal of Infrared, Millimeter and Terahertz Waves》2009,30(7):709-716
In this paper, we propose an optimization method based on Particle Swarm Optimization (PSO) algorithm for thinning a large
multiple concentric circular ring array of uniformly excited isotropic antennas and generate a pencil beam in the vertical
plane with minimum relative side lobe level (SLL). The half-power beam width of the pattern is attempted to make equal to
that of a fully populated array of same size and shape. The synthesis is performed with a standard particle swarm optimization
technique as well as with an improved version of standard PSO. Simulation results of the proposed thinned array are compared
with a fully populated array to illustrate the effectiveness of our proposed method. 相似文献
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基于全波仿真得到的广义阵元有源方向图,该文提出一种用于综合多方向图共形阵列的新方法:自适应动态Meta粒子群优化(ADMPSO)算法。在传统Meta粒子群优化(MPSO)算法基础上,定义了优势子群和非优子群的概念,并通过植入非优子群裁减、优势子群规模膨胀以及惯性权重自适应更新等机制,实现了优化过程中多子群的自适应动态调整,全面提高了算法性能。ADMPSO成功用于12元微带锥面共形阵列非赤道面的多方向图综合,综合过程考虑了由共形载体导致的阵元极化指向各异特征,在公共激励存在约束情况下,使阵列同时实现了笔形、平顶,以及余割平方波束总功率方向图,其与该阵列全波数值仿真完全吻合,优化结果和收敛速度相比于其他算法均有显著改善。 相似文献
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We propose an improvement of particle swarm optimization (PSO) based on the stabilization of particle movement (PM). PSO uses a stochastic variable to avoid an unfortunate state in which every particle quickly settles into a unanimous, unchanging direction, which leads to overshoot around the optimum position, resulting in a slow convergence. This study shows that randomly located particles may converge at a fast speed and lower overshoot by using the proportional‐integralderivative approach, which is a widely used feedback control mechanism. A benchmark consisting of representative training datasets in the domains of function approximations and pattern recognitions is used to evaluate the performance of the proposed PSO. The final outcome confirms the improved performance of the PSO through facilitating the stabilization of PM. 相似文献