Application of best-dimension mutation particle swarm optimization algorithm in sensor scheduling

CHEN Zhi-guo1; FU Yi1; XU Wen-bo1; SUN Jun1

doi:10.13873/j.1000-97872011.12.031

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Application of best-dimension mutation particle swarm optimization algorithm in sensor scheduling

更新时间：2025-09-11

- Application of best-dimension mutation particle swarm optimization algorithm in sensor scheduling
- Vol. 30, Issue 12, Pages: 145-148+152(2011)
- 作者机构：
  
  1. 江南大学物联网工程学院轻工过程先进控制教育部重点实验室
  2. 无锡环境科学与工程研究中心
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872011.12.031
  CLC： TP212;TP301.6
- Published：2011
- 稿件说明：
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[1]陈志国,傅毅,须文波,孙俊.最佳维变异粒子群优化算法在传感器调度中应用[J].传感器与微系统,2011,30(12):145-148+152.

CHEN Zhi-guo1, FU Yi1, XU Wen-bo1, et al. Application of best-dimension mutation particle swarm optimization algorithm in sensor scheduling[J]. 2011, 30(12): 145-148+152.
[1]陈志国,傅毅,须文波,孙俊.最佳维变异粒子群优化算法在传感器调度中应用[J].传感器与微系统,2011,30(12):145-148+152. DOI： 10.13873/j.1000-97872011.12.031.

CHEN Zhi-guo1, FU Yi1, XU Wen-bo1, et al. Application of best-dimension mutation particle swarm optimization algorithm in sensor scheduling[J]. 2011, 30(12): 145-148+152. DOI： 10.13873/j.1000-97872011.12.031.

摘要

粒子群优化（PSO）算法的主要特点是能快速得到问题的解

缺点是容易陷入局部最优。提出了一种利用最佳维变异技术和量子理论方法改进的PSO算法

并应用于目标跟踪传感器调度问题。目标的动力学模型为线性高斯模型

传感器观测值被高斯噪声污染并与目标状态线性相关。对于多传感器单目标跟踪的数学问题

引入提出的最佳维变异PSO算法

在整个时间轴上产生最小成本。仿真实验结果表明:提出的算法比已有的算法收敛速度更快

全局搜索能力更强

传感器调度效率更高。

Abstract

The main characteristic of the particle swarm optimization（PSO） algorithm is that it can get the solution quickly

and its disadvantage is easy to fall into a local optimum in the process.An improved PSO algorithm is proposed using best dimension mutation technique and quantum theory.It is applied to the sensor scheduling problem for target tracking.The dynamics model of the target is linear Gaussian model

and the sensor measurements impaired by white Gaussian noise are linearly related to the state of the target.A numerical problem of single target tracking with multiple sensors is studied using the proposed best dimension mutation particle swarm optimization（BDMPSO） algorithm to get the minimal cost on the whole timeline.The simulation results show that the proposed algorithm has much faster convergence

stronger global search ability and more scheduling efficient than the existing algorithms.

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