【摘要】 无线传感器网络(WSN)是一种由节点组成的无线自组织网络,在很多领域中有广泛的使用。节点定位是无线传感器网络中最重要的部分,使用无测距定位算法中传统的DV-Hop算法来定位误差较大。为了提高DV-Hop算法的精确度,提出了一种基于遗传算法和二进制蚁群算法来改进DVHop定位算法。遗传算法中利用了线性交叉和非均匀变异算子在种群中进行搜索,在此基础上,采用二进制蚁群算法进行进一步的搜索,而后比较适应度函数来保留较优的个体,从而产生了新一代种群。二进制蚁群算法中使得每只蚂蚁的智能化比较低,每条路径对应的存储空间相对较小,显著提高了计算效率。仿真的结果表明,提出的算法比传统的DV-Hop算法、基于遗传算法的DV-Hop算法有更快的收敛速度和更高的定位精度。更多还原

【Abstract】 Wireless sensor networks( WSN) is a wireless self-organizing network consisting of nodes,which was widely used in many fields. Node location is the most important part of wireless sensor network. The traditional DV-Hop algorithm without location-based location algorithm had large error. In order to improve the accuracy of DV-Hop algorithm,a genetic algorithm and binary ant colony algorithm was proposed to improve DV-Hop localization algorithm. Genetic algorithm used linear crossover and inhomogeneous mutation operator in the population search,on this basis,binary ant colony algorithm was used for further search,and then better individuals was retained by comparing the fitness function,resulting in a new generation of populations. The binary ant colony algorithm made each ant relatively low intelligence,and the corresponding storage space of each path was relatively small,which greatly improved the computational efficiency. The simulation results show that the proposed algorithm has faster convergence speed and higher positioning accuracy than the traditional DV-Hop algorithm and the genetic algorithm-based DV-Hop algorithm.更多还原