微粒群优化算法在基站分布规划中的应用  

The Application of Particle Swarm Optimization Algorithm in BS Planning

作　　者：陈存香[1] 王俊峰[1] 

机构地区：[1]北京交通大学电子信息工程学院,北京100044

出　　处：《电脑与电信》2009年第6期45-46,50,共3页Computer & Telecommunication

摘　　要：微粒群优化算法(PSO)是一种具有全局随机优化的智能算法。该算法编程简单,鲁棒性强,并行运算能力强,能以较快的速度收敛至全局最优解。本文在PSO的思想基础上提出了一种改进搜索方向,降低"早熟"概率的方法,即结合遗传算法,引入了爬坡算子。本文并将该改进算法应用于基站分布规划的研究中,实验仿真结果表明,利用该改进算法能提高基站覆盖率,降低经济成本。Particle Swarm Optimization is intelligent algorithm with global random optimization. It provides with simple programs, strong robust, efficient parallel computing and faster speed to global optimal solution. Based on the idea of PSO, a new method is put forword. That is, for improving search direction and reducing the probability of＂ prematurity ＂, it introduces climbing operator with combination of genetic algorithm. Also, this improved algorithm applies to the base station＇s planning. The experimental simulation result shows that the use of improved algorithm can increase the coverage of base stations and reduce the economic cost.

关 键 词：PSO算法 基站分布规划 爬坡算子 

分 类 号：O242.23[理学—计算数学] TP24[理学—数学]