Levy飞行策略结合三维灰狼优化的DV-hop定位算法

DV-hop Localization Algorithm Based on Levy Flight Strategy and 3D Grey Wolf Optimization

针对无线传感器网络节点部署不均衡,极大似然值估计算法对待定位节点位置计算精确度较低的问题,提出一种Levy飞行策略结合三维灰狼优化的DV-hop定位算法.首先引入节点间不同跳数权重因子解决节点部署不均衡问题;其次在灰狼算法基础上结合Levy飞行策略,对灰狼群每次位置迭代更新进行优化,并推广到三维空间;最后利用改进后的智能仿生三维灰狼算法对每一个待定位节点位置进行寻优定位计算,进一步提高待定位节点位置计算精度.实验结果表明,该算法优于经典DV-hop算法、三维加权DV-hop算法以及未使用飞行策略的灰狼算法,验证了该算法在待定节点定位的准确性.

Due to the asymmetrical distribution of wireless sensor network nodes in the actual distribution and Maximum Likelihood Estimate used to deal with the problem of low accuracy of location calculation,a DV-Hop positioning algorithm based on Levy flight strategy and 3D Grey Wolf optimization is proposed.Firstly,weight factors are introduced in different hops between nodes to avoid the use of the mean hops of punctuation marks directly by the nodes to be fixed.Secondly,based on the Grey Wolf algorithm and combined with the flight strategy,the iterative updating of each position of the grey Wolf group was optimized and extended to the three-dimensional space.Finally,the improved intelligent bionic three-dimensional Grey Wolf algorithm is used to optimize the location calculation of each node to further improve the position calculation accuracy of the node.The results show that the proposed algorithm is better than the classical DV-Hop algorithm,the 3D weighted DV-hop algorithm and the Grey Wolf algorithm without flight strategy,and the accuracy of the proposed algorithm is verified.