基于混合人工蜂群策略的改进DV-Hop定位算法

为了减小DV-Hop算法在无线传感器网络节点定位中的误差，提出了一种基于混合人工蜂群算法的改进算法。该算法结合了粒子群算法收敛速度快和蜂群算法搜索能力强的特性，首先通过DV-Hop算法估计锚节点与未知节点之间的距离，然后采用粒子群算法计算未知节点的初始位置，最后利用蜂群算法进行迭代求精，从而实现基于不同距离测量方法的总体优化。仿真结果表明，改进算法的定位精度较DV-Hop算法和基于粒子群的定位算法有明显改善。

Improved Localization Algorithm of DV-Hop Based on Hybrid Artificial Bee Colony Algorithm

In order to reduce the node localization error of DV-Hop algorithm in Wireless Sensor Network(WSN), an improved algorithm based on Hybrid Artificial Bee Colony(HABC) algorithm was put forward. Combining the fast convergence speed characteristics of Particle Swarm Optimization(PSO) and strong searching capability of ABC, improved algorithm firstly estimating the distance between unknown nodes and anchor-nodes by DV-Hop algorithm. Secondly, the initial position of unknown nodes was calculated by PSO. Finally, the iterative numerical method with the initial values of estimated node locations was presented by ABC. It can be concluded that the improved algorithm has obviously better locating precision than PSO and DV-Hop.