A self-adaptive harmony search localization algorithm based on MCB

SUN Chong; SUN Zi-wen

doi:10.13873/J.1000-9787(2015)04-0119-04

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A self-adaptive harmony search localization algorithm based on MCB

更新时间：2025-09-11

- A self-adaptive harmony search localization algorithm based on MCB
- Vol. 34, Issue 4, Pages: 119-122(2015)
- 作者机构：
  
  江南大学物联网工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2015)04-0119-04
  CLC： TN929.5;TP212.9
- Published：2015
- 稿件说明：
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[1]孙崇,孙子文.一种基于MCB的自适应和声搜索定位算法[J].传感器与微系统,2015,34(04):119-122.

SUN Chong, SUN Zi-wen. A self-adaptive harmony search localization algorithm based on MCB[J]. 2015, 34(4): 119-122.
[1]孙崇,孙子文.一种基于MCB的自适应和声搜索定位算法[J].传感器与微系统,2015,34(04):119-122. DOI： 10.13873/J.1000-9787(2015)04-0119-04.

SUN Chong, SUN Zi-wen. A self-adaptive harmony search localization algorithm based on MCB[J]. 2015, 34(4): 119-122. DOI： 10.13873/J.1000-9787(2015)04-0119-04.

摘要

针对无线传感器网络锚节点稀疏条件下节点定位中存在的翻转现象和定位精度问题

提出了一种基于MCB的自适应和声搜索定位算法。通过引入MCB算法中的采样思想

随机产生网络拓扑约束下的未知节点的坐标

引入自适应的和声保留概率和音调调节概率

达到提高搜索能力和定位精度目的。仿真结果表明:算法能有效解决翻转现象

提高定位精度

提出的算法在定位精度和计算量方面优于对比算法。

Abstract

Aiming at problem of flip ambiguity and localization precision

a self-adaptive harmony search algorithm for wireless sensor networks（ WSNs） node localization is proposed. Based on the Sampling Theory of Monte Carlo localization boxed（ MCB）

the coordinates of unknown nodes are generated randomly under the restriction of the network topology constraints

in order to improve the search ability and the localization precision

introduce adaptive harmony memory considering rate（ HMCR） and pitch adjusting rate（ PAR）. Simulation results show that the algorithm can effectively address the flip ambiguity and improve the localization precision

and the algorithm outperforms

in terms of localization precision and computational complexity compared with otherst.

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references

基于信度和早熟检验的混沌粒子群优化定位算法 [J]. 孙子文,王鑫雨,白勇,纪志成. 传感器与微系统 . 2013(09)

改进的多目标和声搜索算法 [J]. 乔英,高岳林,江巧永. 计算机工程 . 2012(18)

和声搜索算法研究进展 [J]. 雍龙泉. 计算机系统应用 . 2011(07)

一种新的多目标改进和声搜索优化算法 [J]. 刘思远,柳景青. 计算机工程与应用 . 2010(34)

An improved global-best harmony search algorithm for faster optimization [J] . Wan-li Xiang,Mei-qing An,Yin-zhen Li,Rui-chun He,Jing-fang Zhang. Expert Systems With Applications . 2014

A two-objective evolutionary approach based on topological constraints for node localization in wireless sensor networks [J] . Massimo Vecchio,Roberto López-Valcarce,Francesco Marcelloni. Applied Soft Computing Journal . 2011 (7)

Monte Carlo localization for mobile wireless sensor networks [J] . Aline Baggio,Koen Langendoen. Ad Hoc Networks . 2007 (5)

An improved harmony search algorithm for solving optimization problems [J] . M. Mahdavi,M. Fesanghary,E. Damangir. Applied Mathematics and Computation . 2006 (2)

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