[1]张中芳,张玲华.基于量子粒子群优化的DV-Hop 算法研究[J].计算机技术与发展,2018,28(05):81-85.[doi:10.3969/j.issn.1673-629X.2018.05.019]
　ZHANG Zhong-fang,ZHANG Ling-hua.Research on DV－Hop Algorithm Based on Quantum Particle Swarm Optimization[J].,2018,28(05):81-85.[doi:10.3969/j.issn.1673-629X.2018.05.019]
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基于量子粒子群优化的DV-Hop 算法研究()
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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:
81-85
栏目:
智能、算法、系统工程
出版日期:
2018-05-10

文章信息/Info

Title:
Research on DV－Hop Algorithm Based on Quantum Particle Swarm Optimization
文章编号:
1673－629X(2018)05－0081－05
作者:
张中芳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):
ZHANG Zhong-fang 1 ; ZHANG Ling-hua 2
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:
粒子群算法; 量子粒子群算法; DV－Hop 定位算法; 高速收敛; 无线传感器网络
Keywords:
particle swarm optimization; quantum particle swarm optimization; distance vector－hop; high－speed convergence; wireless sensor network
分类号:
TP301.6
DOI:
10.3969/j.issn.1673-629X.2018.05.019
文献标志码:
A
摘要:
粒子群算法是一种参数少，形式简单的群体智能算法，但存在局部收敛和参数选择困难的缺陷，因此融合其他的智能方式来处理这些缺陷，提出一种具有量子行为的粒子群优化算法。针对无线传感器网络(WSN)定位算法中经典DV-Hop 算法运用最小二乘法的估计误差过大、粒子群(PSO)算法易陷入局部最优的问题，提出了一种改进量子粒子群算法与DV－Hop 的融合算法。该算法采用具有量子行为的粒子群算法(QPSO)来代替粒子群算法，利用该算法易收敛于全局最优值和高速收敛性的特点，对 DV－Hop 算法中未知节点的估计结果进行优化和修正。仿真结果表明，相比传统 DV－Hop 和PSO－DVHop 算法，该算法的定位精度高，稳定性好，收敛速度快，具有一定的优越性和可行性。
Abstract:
Particle swarm optimization (PSO) is a group of intelligent algorithm with few parameters and simple form，which has some difficulties in local convergence and parameter selection．Therefore，combined with other intelligent methods to deal with these defects，we propose a particle swarm optimization algorithm with quantum behavior．Aiming at the problems that the estimation error of the least square method in classical distance vector－hop (DV－Hop) is too large and the particle swarm optimization (PSO) easily traps into local
optimum in the wireless sensor network (WSN)，we put forward a fusion algorithm of improved quantum particle swarm optimization (QPSO) and DV－Hop．It uses the particle swarm optimization with quantum behavior instead of the basic particle swarm optimization，which optimizes and modifies the estimation result of unknown nodes in the DV－Hop by using its characteristics of convergence to the global optimal value and high－speed convergence．The simulation shows that compared with the traditional DV－Hop and PSO－DVHop，the proposed algorithm has high positioning accuracy with good stability and fast convergence speed，and is superior and feasible．

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更新日期/Last Update: 2018-06-28