鸡群协同定位算法的研究  

Research on Chicken Swarm Cooperative Localization Algorithm

作　　者：于明洁 夏斌[1] 张立晔 YU Mingjie;XIA Bin;ZHANG Liye(School of Computer Science and Technology,Shandong University of Technology,Zibo 255020,China)

机构地区：[1]山东理工大学计算机科学与技术学院,山东淄博255020

出　　处：《无线电通信技术》2023年第3期489-494,共6页Radio Communications Technology

基　　金：国家自然科学基金(62001272)

摘　　要：无线传感器网络(Wireless Sensor Network,WSN)定位问题可以看作一个目标函数最优化问题,通过鸡群优化算法进行求解。传统的鸡群定位算法所用的目标函数只考虑了待定位节点和参考节点之间的测量距离,测量距离不完整限制了定位精度的提高。基于此提出了一种鸡群协同定位算法,首先改进目标函数,确保待定位节点之间的测量距离被充分使用;然后采取多维标度(Multidimensioal Scaling,MDS)方法提供的良好初始位置,从而提高定位算法的收敛速度。仿真实验结果表明,与传统鸡群定位算法、粒子群定位算法、灰狼定位算法和改进的鸡群定位算法相比,鸡群协同定位算法能有效提高定位精度。另外,与传统的鸡群定位算法相比,虽然鸡群协同定位算法的时间复杂度有所增加,但定位性能得到了改善。Localization problem of Wireless Sensor Network(WSN)can be regarded as an optimization problem of objective function,and then solved by chicken swarm optimization algorithm.The objective function used in traditional chicken swarm localization algorithm only considers the measured distance between the node to be located and the reference node.And the incomplete measurement distance limits the improvement in localization accuracy.Therefore,a chicken swarm cooperative localization algorithm is proposed.Firstly,the objective function is improved to ensure that the measured distance between the nodes to be located is fully used.Then,a Multidimensional Scaling(MDS)method is used to provide an excellent initial position and improve the convergence speed of the localization algorithm.Simulation results show that the chicken swarm cooperative localization algorithm can effectively improve localization accuracy compared with traditional chicken swarm localization algorithm,particle swarm localization algorithm,grey wolf localization algorithm and improved chicken swarm localization algorithm.In addition,compared with traditional chicken swarm localization algorithm,although the time complexity of the chicken swarm cooperative localization algorithm has increased,the localization performance has been improved.

关 键 词：鸡群优化 协同定位 定位精度 目标函数 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]