基于鸡群算法的无线传感器网络定位研究

Research on Wireless Sensor Network Location Based on Chicken Swarm Optimization

针对无线传感器网络(WSN)节点定位精度不足的问题,提出一种改进鸡群算法与典型定位模型相结合的ICSO(Improve Chicken Swarm Optimization)算法。首先,提出基于pareto距离分级的分类算法,优化鸡群算法种群比例;然后,在母鸡位置公式中引入随机游走策略,增大搜索范围;最后,将净能量增益引入小鸡的位置公式,进一步提高定位精度。仿真结果表明,ICSO与改进后的粒子群算法(MPSO)和鸡群算法(BIDCSO)相比,在参考节点比例、节点密度、通信半径和定位区域面积等方面的平均定位精度分别提高了19.2%、22.1%、12.1%、8.5%和6%、10.5%、4.4%、4.7%。实验结果表明,ICSO算法能够有效提高定位精度。

Aiming at the problem of insufficient positioning accuracy of wireless sensor network(WSN)nodes,an improved algorithm of integrated Chicken Swarm Optimization(ICSO)is proposed. Firstly,a classification algorithm based on pareto distance grading is proposed to optimize the population ratio of flock algorithm. Then,a random walk strategy is introduced in the hen position formula to increase the search range. Finally,the net energy gain is introduced into the position formula of the chick. Further improve the positioning accuracy.The simulation results show that compared with the Modified particle swarm optimization(MPSO)and Bio Inspired Distributed Chicken Swarm Optimization(BIDCSO),the average positioning accuracy of ICSO in terms of the proportion of reference nodes,node density,communication radius and location area is improved by 19.2%,22.1%,12.1%,8.5% and 6%,10.5%,4.4%,4.7%,respectively. Experimental results show that the ICSO algorithm can effectively improve the positioning accuracy.