针对当前无线传感器网络定位算法存在精度不足、定位时间长的问题,提出一种基于改进灰狼优化器的定位方法。针对基本灰狼优化器求解精度不足、易陷入局部最优等缺陷,引入自适应搜索机制来扩展算法的搜索范围,并使用螺旋搜索技术来帮助算法跳出局部最优。传感器节点基于信号强度估算与信号发送端的距离,使用改进的灰狼优化器估算位置。与现有的定位方法相比,所提出的定位算法具有更好的定位精度与收敛速度。

To address the problems of insufficient localization accuracy and long localization time of current wireless sensor network localization algorithms,a localization method based on the improved gray wolf optimizer is proposed.To address the shortcomings of the basic gray wolf optimizer such as insufficient solution accuracy and easy to fall into local optimum,an adaptive search mechanism is introduced to extend the search range of the algorithm,and a spiral search technique is used to help the algorithm jump out of local optimum.The sensor nodes estimate the distance to the signal sender based on the signal strength and estimate the position using the improved gray wolf optimizer.Compared with existing localization methods,the proposed localization algorithm has better localization accuracy and convergence speed.