WSN杂交粒子群优化定位改进算法

Improved hybrid particle swarm optimization location algorithms in WSN

确定无线传感器网络(WSN)中节点的位置是网络初始化阶段首要任务之一,杂交PSO定位算法(Hybrid Particle Swarm Optimization,HPSO)可以通过父代粒子杂交产生性能较好的子代粒子,加速收敛,其定位精度明显优于其他PSO算法,因而成为近年来研究的热点之一。然而,杂交PSO定位算法存在计算量大、定位时间长等问题,针对这些问题,提出一种改进杂交PSO定位算法(Improved Hybrid PSO,IHPSO),在交叉和变异过程中引入粒子集中度和粒子平稳度两个概念,并加入选择机制和排队机制,加快算法的收敛度,同时有效地提高算法的定位精度和定位时间。

Determining the location of nodes in wireless sensor networks(WSN)is one of the most important tasks in the initial stage of the network.The location method based on PSO(Particle Swarm Optimization)principle can effectively improve the location accuracy,so it has become one of the hotspots in recent years.Particularly,hybrid PSO location algorithm(HPSO)can generate better offspring particles through parent-generation particle hybridization and accelerate convergence,so its positioning accuracy is obviously better than other PSO algorithms.However,the hybrid PSO location algorithm has many problems,such as large amount of computation,long location time and so on.To solve these problems,an improved hybrid PSO(IHPSO)localization algorithm is proposed.Selection mechanism and queuing mechanism are introduced in the crossover process,which effectively improves the positioning accuracy and positioning time of the algorithm.