基于蚁群粒子群混合的无线传感器网络定位算法

摘要

DY-Hop is one of the typical localization algorithms in Wireless Sensor Network, and the hybrid of the Ant Colony Optimization and Particle Swarm Optimization (ACOPSO) is used as a global optimization functions generally. In order to reduce the positioning error and improve the location accuracy, the new algorithm combined ACOPSO with DV-Hop, DV-Hop is used to estimate the measuring distance between unknown nodes and anchor nodes, ACOPSO is used to minimise the fitness function that related to DV-Hop, Accordingly optimize the algorithm based on different distance or path. Simulation by the MATLAB environment indicated that the new algorithm have smaller average positioning error than DV- HOP algorithm and based on Particle Swarm Optimization (PSO), it improved the location accuracy effectively.%在无线传感器网络免于测距的定位算法中,DV-Hop算法是典型算法之一,蚁群粒子群算法(ACOPSO)通常被用来作全局优化;为了降低定位误差,提高定位精度,新算法先用DV-Hop算法估量未知节点与锚节点的测量距离,蚁群粒子群算法(ACOPSO)作后期优化,最小化DV-Hop的适应度函数,从而实现基于不同的距离或路径测量方法的优化;经过Matlab仿真分析表明,在相同的仿真环境中,新算法产生的平均定位误差比EV-Hop算法和基于粒子群的定位算法产生的平均定位误差更低,有效地提高了定位精度.