Research on localization technology based on quantum-behaved particle swarm optimization algorithm

ZHAO Ji1; JI Zhi-cheng1

doi:10.13873/j.1000-97872012.05.027

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Research on localization technology based on quantum-behaved particle swarm optimization algorithm

更新时间：2025-09-11

- Research on localization technology based on quantum-behaved particle swarm optimization algorithm
- Vol. 31, Issue 5, Pages: 58-61(2012)
- 作者机构：
  
  1. 江南大学电气自动化研究所
  2. 无锡城市职业技术学院电子信息工程系
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872012.05.027
  CLC： TN929.5;TP212.9
- Published：2012
- 稿件说明：
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[1]赵吉,纪志成.基于量子行为粒子群优化算法的定位技术研究[J].传感器与微系统,2012,31(05):58-61.

ZHAO Ji1, JI Zhi-cheng1. Research on localization technology based on quantum-behaved particle swarm optimization algorithm[J]. 2012, 31(5): 58-61.
[1]赵吉,纪志成.基于量子行为粒子群优化算法的定位技术研究[J].传感器与微系统,2012,31(05):58-61. DOI： 10.13873/j.1000-97872012.05.027.

ZHAO Ji1, JI Zhi-cheng1. Research on localization technology based on quantum-behaved particle swarm optimization algorithm[J]. 2012, 31(5): 58-61. DOI： 10.13873/j.1000-97872012.05.027.

摘要

针对无线传感器网络（WSNs）节点定位问题

阐述了WSNs的分布迭代式定位方法研究。这种方法将每次迭代后定位的节点作为其余未知节点的参考节点.同时将基于测距定位问题看成一个多维优化问题

并提出利用具有快速收敛能力的量子行为粒子群优化（QPSO）算法进行求解。最后将仿真实验结果与粒子群优化（PSO）算法进行比较

表明QPSO算法在优化性能上优于PSO算法

有效提高了节点定位精度

证明该方法的有效性。

Abstract

Aiming at the problem of node localization in wireless sensor networks（WSNs）

the distributed iterative localization method research of WSNs is described.Once the nodes get localized

they act as references for the rest of nodes.The ranging-based localization problem is formulated as a multi-dimensional optimization issue

and the quantum-behaved particle swarm optimization algorithm（QPSO） which has quick convergence ability is used for solutions.The simulation results are compared with the particle swarm optimization（PSO） algorithm

it shows that QPSO algorithm outperforms the PSO algorithm and improve the node localization precision effectively

which improves the validity of the presented method.

关键词

Keywords

references

基于粒子群优化的无线传感器网络节点定位改进 [J]. 陈星舟,廖明宏,林建华. 计算机应用 . 2010(07)

量子行为粒子群优化算法研究 [D]. 孙俊. 江南大学 2009

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