Monte Carlo node localization algorithm based on quantum genetic

TIAN Hao-shan

doi:10.13873/j.1000-9787(2017)09-0125-04

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Monte Carlo node localization algorithm based on quantum genetic

更新时间：2025-09-11

- Monte Carlo node localization algorithm based on quantum genetic
- Vol. 36, Issue 9, Pages: 125-128+135(2017)
- 作者机构：
  
  兰州交通大学电子与信息工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-9787(2017)09-0125-04
  CLC： TN929.5;TP18;TP212.9
- Published：2017
- 稿件说明：
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[1]田浩杉.基于量子遗传的蒙特卡洛节点定位算法[J].传感器与微系统,2017,36(09):125-128+135.

TIAN Hao-shan. Monte Carlo node localization algorithm based on quantum genetic[J]. 2017, 36(9): 125-128+135.
[1]田浩杉.基于量子遗传的蒙特卡洛节点定位算法[J].传感器与微系统,2017,36(09):125-128+135. DOI： 10.13873/j.1000-9787(2017)09-0125-04.

TIAN Hao-shan. Monte Carlo node localization algorithm based on quantum genetic[J]. 2017, 36(9): 125-128+135. DOI： 10.13873/j.1000-9787(2017)09-0125-04.

摘要

针对无线传感器网络（WSNs）节点定位的问题

提出了一种量子遗传算法与蒙特—卡洛相结合的定位算法（QGA-MCL）。将QGA应用于MCL中的采样过滤阶段

通过合理的编码方案、译码方案以及量子旋转门对采样区域中随机产生的量子染色体进行操作

提高了样本寻优效率和定位精度

并加快了算法的收敛速度。仿真结果表明:与蒙特—卡洛定位算法相比

提出的QGA-MCL算法能够减少约10.2%的定位误差

同时

算法的收敛速度也得到了显著提升。

Abstract

A new node localization algorithm is proposed for wireless sensor networks（ WSNs）

which combines quantum genetic algorithm with the Monte Carlo localization（ QGA-MCL）. QGA is applied to the sampling filtering stage in MCL. Operate the quantum chromosomes which is random generated in sampling area by reasonable encoding scheme

decoding scheme and quantum rotating gate. Simulation results demonstrate that compared with Monte Carlo localization algorithm

the proposed QGA-MCL algorithm can reduce positioning error about 10. 2 %

and meanwhile

the convergence speed of algorithm is improved significantly.

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references

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