基于坐标优化的FOA-Amorphous节点定位算法

FOA-Amorphous Node Localization Algorithm Based on Coordinate Optimization

节点的位置信息在无线传感器网络的定位中起着至关重要的作用,而Amorphous算法的节点定位精度低。针对影响Amorphous定位精度的主要原因分析,提出了一种基于坐标优化的FOA-Amorphous节点定位算法。首先,引入多通信半径的概念细化节点跳数,利用网络平均连通度对节点的平均跳距进行重算;然后,以极大似然估计法得到的未知节点坐标值为果蝇优化算法中各果蝇的初始位置,通过此初始位置产生每个果蝇的初始种群,代入适应度函数求得当前果蝇的最佳位置,引入了个体认知因子c 1和群体引导因子c 2,优化了果蝇随机搜索的距离和方向,使得算法快速收敛到全局最优,避免算法早熟,提高了算法的收敛精度,通过迭代找到最佳未知节点位置坐标。与双通信半径算法、PSO-IDV-Hop算法以及Amorphous算法相比,该算法的归一化定位误差分别降低了约7%、23%和44%。

The location information of nodes plays a crucial role in the location of wireless sensor networks,and the node location accuracy of Amorphous algorithm is low.Aiming at the main reasons that affect the accuracy of Amorphous location,a FOA-Amorphous node location algorithm based on coordinate optimization is proposed.Firstly,the concept of multi-communication radius is introduced to refine the number of node hops,the average network connectivity is used to recalculate the average distance per hop of the node.Then,the coordinate value of the unknown node obtained by the maximum likelihood estimation method is used as the initial position of each fruit fly in the fruit fly optimization algorithm,the initial population of each fruit fly is generated through this initial position,and the optimal position of the current fruit fly is obtained by substituting the fitness function.Individual cognitive factor c 1 and group guidance factor c 2 are introduced to optimize the distance and direction of the random search of the fruit fly,so that the proposed algorithm quickly converges to the global optimum,avoids its premature maturity,improves its convergence accuracy,and finds the optimal unknown node position coordinates through iteration.Compared with the dual communication radius algorithm,the PSO-IDV-Hop algorithm and the Amorphous algorithm,the normalized positioning errors are reduced by about 7%,23%and 44%,respectively.