[1]汪晨,张玲华.基于人工鱼群算法的改进质心定位算法[J].计算机技术与发展,2018,28(05):103-106.[doi:10.3969/ j. issn.1673-629X.2018.05.024]
　WANG Chen,ZHANG Ling-hua.An Improved Centroid Localization Algorithm Based on Optimized Artificial Fish Swarm Algorithm[J].,2018,28(05):103-106.[doi:10.3969/ j. issn.1673-629X.2018.05.024]
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基于人工鱼群算法的改进质心定位算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年05期
页码:
103-106
栏目:
智能、算法、系统工程
出版日期:
2018-05-10

文章信息/Info

Title:
An Improved Centroid Localization Algorithm Based on Optimized Artificial Fish Swarm Algorithm
文章编号:
1673-629X(2018)05-0103-04
作者:
汪晨1 ; 张玲华2
1. 南京邮电大学 通信与信息工程学院,江苏 南京 210003;
2. 江苏省通信与网络技术工程研究中心,江苏 南京 210003
Author(s):
WANG Chen1 ; ZHANG Ling-hua2
1. School of Telecommunication &Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;
2. Communication and Network Technology Engineering Research Center,Nanjing 210003,China
关键词:
无线传感器网络; 人工鱼群算法; 加权质心算法; 定位精度
Keywords:
wireless sensor network; artificial fish swarm algorithm; weighted centroid algorithm; positioning accuracy
分类号:
TN926
DOI:
10.3969/ j. issn.1673-629X.2018.05.024
文献标志码:
A
摘要:
针对传统质心定位算法精度不高的问题,提出了一种基于人工鱼群算法的改进质心定位算法。 人工鱼群算法(AFSA)是一种新型的巡游策略,具有鲁棒性强,全局收敛好以及对初值不敏感等优势,所以将人工鱼群算法与加权质心算法相结合。 利用加权质心算法,先计算锚节点与未知节点间的距离,再根据此距离估算未知节点位置。 根据测得的距离和锚节点位置信息建立适应度函数,利用该算法具有收敛于全局最优值的特点,不断迭代寻优,对未知节点的估计结果进行优化和修正,从而提高节点的定位精度。 实验结果表明,与一般质心定位算法相比,虽然在一定程度上增加了一些计算开销,但该算法的定位精度更高,稳定性更好,收敛速度更快,具有优越性和可行性。
Abstract:
We propose an improved centroid localization algorithm based on artificial fish swarm algorithm,aiming at the problem that the accuracy of the traditional centroid localization algorithm is not high. Artificial fish swarm algorithm (AFSA) is a new type of parade strategy,with strong robustness,perfect global convergence and the initial value of the insensitivity and other advantages,which will be combined with the weighted centroid algorithm. The distance between the anchor node and the unknown node is calculated by the weighted centroid algorithm,by which the position of the unknown node is estimated. And then the measured distance and anchor node position information are utilized to establish the fitness function. The estimation result of the unknown node is optimized and modified by iterative optimization according to the characteristic of the proposed algorithm converging to the global optimal. The experiments show that compared with the general centroid localization algorithm,although the proposed algorithm increases computational cost to a certain extent,it is superior and feasible with better high positioning accuracy,better stability and faster convergence speed.

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更新日期/Last Update: 2018-07-02