A Model of Distributed Sensors' Scheduling and Self-Adaptive Probability Particle Swarm Optimization Algorithm

REN Jun-liang; XING Qing-hua; LI Long-yue; JIA Zhe

doi:10.3969/j.issn.0372-2112.2015.09.012

您当前的位置：

首页 >

文章列表页 >

A Model of Distributed Sensors' Scheduling and Self-Adaptive Probability Particle Swarm Optimization Algorithm

更新时间：2025-07-16

- A Model of Distributed Sensors' Scheduling and Self-Adaptive Probability Particle Swarm Optimization Algorithm
- Acta Electronica Sinica Vol. 43, Issue 9, Pages: 1756-1762(2015)
- 作者机构：
  
  1. 空军工程大学防空反导学院,陕西,西安,710051
  2. 空军指挥学院,北京,100097
  3. 空军工程大学防空反导学院,陕西,西安,710051
  4. 空军指挥学院,北京,100097
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2015.09.012
  CLC： TP391.1
- Published：2015
- 稿件说明：
移动端阅览
REN Jun-liang, XING Qing-hua, LI Long-yue, et al. A Model of Distributed Sensors' Scheduling and Self-Adaptive Probability Particle Swarm Optimization Algorithm[J]. Acta Electronica Sinica, 2015, 43(9): 1756-1762.
DOI：

REN Jun-liang, XING Qing-hua, LI Long-yue, et al. A Model of Distributed Sensors' Scheduling and Self-Adaptive Probability Particle Swarm Optimization Algorithm[J]. Acta Electronica Sinica, 2015, 43(9): 1756-1762. DOI： 10.3969/j.issn.0372-2112.2015.09.012.

摘要

不同体制的多个传感器通常部署于不同位置

据此采用分布式计算思想研究其调度问题.设计了传感器指控模块和传感器模块

探讨两者间的信息交互过程

给出基于最小调度时间间隔的传感器探测任务分解方法

建立传感器探测目标的匹配度计算模型.针对调度方案生成子模块设计了一种自适应概率粒子群算法

算法中粒子的分量根据方案适应值大小以不同的概率取相应值

体现粒子在迭代过程中的思考.实例分析表明

该算法能在迭代前期较快地收敛到一个较优值

这一特点使得在迭代次数有限的情况下

算法仍可获得较好的调度方案

满足调度方案实时高效的要求.

Abstract

This paper introduced a distributed computing method to study the scheduling problem in multi-sensor systems.According to the features of these sensors which were usually deployed at different locations

it redesigned C2 (Command and Control) module and sensor module

and discussed information interaction procedure between two modules.Then it established a sensor-target detection match degree computing model which involved task decomposition and minimal scheduling period methods.A self-adaptive probability particle swarm optimization (SAPPSO) algorithm for scheduling program was given.In SAPPSO

particle fitness value was based on different probabilities

which reflected the thinking of particle during iteration process.Experimental results showed that SAPPSO algorithm converged quickly

especially in the previous iteration period

which enabled SAPPSO to fulfill the requirements of real-time and high efficiency for scheduling.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

SADE:An Integrated Development Environment for Self-Adaptive Internetwares

Analysis and Improvement of a Self-Adaptive Image Encryption Algorithm

Algebraic Operations on Bloom Filters

Related Author

FONT face

Verdana

MAO Xin-jun

LI Xue-si

YIN Jun-wen

DONG Meng-gao

HU Cui-yun

WU Bin

Related Institution

（国防科学技术大学计算机学院，）

College of Computer Science, Chongqing University

College of Computer Science & Technology,Beijing University of Technology

College of Electronic Information & Control Engineering,Beijing University of Technology

School of Computer Science,Communication University of China

⁰