[1]彭 铎,张 倩,张腾飞,等.基于坐标优化的 FOA-Amorphous 节点定位算法[J].计算机技术与发展,2023,33(07):91-97.[doi:10. 3969 / j. issn. 1673-629X. 2023. 07. 014]
 PENG Duo,ZHANG Qian,ZHANG Teng-fei,et al.FOA-Amorphous Node Localization Algorithm Based on Coordinate Optimization[J].,2023,33(07):91-97.[doi:10. 3969 / j. issn. 1673-629X. 2023. 07. 014]
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基于坐标优化的 FOA-Amorphous 节点定位算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
33
期数:
2023年07期
页码:
91-97
栏目:
移动与物联网络
出版日期:
2023-07-10

文章信息/Info

Title:
FOA-Amorphous Node Localization Algorithm Based on Coordinate Optimization
文章编号:
1673-629X(2023)07-0091-07
作者:
彭 铎张 倩张腾飞陈江旭
兰州理工大学 计算机与通信学院,甘肃 兰州 730050
Author(s):
PENG DuoZHANG QianZHANG Teng-feiCHEN Jiang-xu
School of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,China
关键词:
Amorphous 算法坐标优化多通信半径果蝇优化算法认知因子引导因子
Keywords:
Amorphous algorithm coordinate optimization multi - communication radius fruit fly optimization algorithm cognitive factorguidance factor
分类号:
TP393
DOI:
10. 3969 / j. issn. 1673-629X. 2023. 07. 014
摘要:
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 c1 和群体引导因子 c2 ,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% 。
Abstract:
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-Amorphousnode location algorithm based on coordinate optimization is proposed. Firstly,the concept of multi-communication radius is introduced torefine 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 eachfruit fly in the fruit fly optimization algorithm, the initial population of each fruit fly is generated through this initial position,and theoptimal position of the current fruit?
fly is obtained by substituting the fitness function. Individual cognitive factor c1 and group guidancefactor c2 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 nodeposition 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.
更新日期/Last Update: 2023-07-10