基于改进粒子群算法的无线传感器网络节点定位

Node localization in wireless sensor networks based on improved particle swarm optimization

针对传统距离矢量-跳数(DV-Hop)算法中最小二乘法的估计误差过大、粒子群(PSO)算法易陷入局部最优的问题,提出了一种改进粒子群算法与DV-Hop的融合算法。首先从粒子速度、惯性权重、学习策略、变异方面对粒子群算法进行改进,增强算法跳出局部最优的能力,提高迭代后期算法的搜索速度;然后在DV-Hop算法第三阶段采用改进粒子群算法优化节点的定位结果。仿真结果表明:相比传统DV-Hop算法、基于混沌粒子群算法的DV-Hop改进算法(MPSO1-DV-Hop)和基于改进型粒子群优化的DV-Hop算法(MPSO2-DV-Hop),该算法的定位精度高,稳定性好,适用于定位精度和稳定性要求较高的场景。

The estimation error of the least square method in traditional Distance Vector-Hop （DV-Hop） algorithm is too large and the Particle Swarm Optimization （PSO） algorithm easily traps into local optimum. In order to overcome the problems, a fusion algorithm of improved particle swarm algorithm and DV-Hop algorithm was presented. First of all, PSO algorithm was improved from aspects of particle velocity, inertia weight, learning strategy and variation, which enhanced the ability of algorithm to jump out of local optimum and increased the search speed of the algorithm in later iterative stage. The node localization result was optimized by using the improved PSO algorithm in the third stage of the DV-Hop algorithm. The simulation results show that compared with the traditional DV-Hop algorithm, the improved DV-Hop based on chaotic PSO algorithm, and the DV-Hop algorithm based on improved PSO, the proposed algorithm has high positioning accuracy, good stability.