摘要
分析了目前军用无人机装备维修任务调度问题的组成及现状,构建了改进的混合粒子群算法,通过离散化粒子群简化粒子论域,加快计算速度;引入浓度监控机制,综合粒子浓度分布和适应度大小两方面信息,对进化过程进行调控;结合遗传算法,增加粒子间的交叉、变异,加快粒子群进化速度,防止陷入局部最优;并在Matlab环境下对图形展示函数进行优化,实现迭代过程动态可视。最后通过实例分析,高效计算得出最佳调度方案,实现了混合粒子群算法在装备资源调度问题的有效应用。
This paper puts forward the constitution and situation of current military maintenance task scheduling problems of unmanned aerial vehicle(UAV) equipment.It builds the advanced hybrid PSO algorithm, conducting discretization to simplify the domain of discourse, speeding up the calculation process.Integrating the particle’s concentration distribution and fitness information by the concentration monitoring method so as to adjust and control the process of evolution.Using genetic algorithm to add crossover and mutation between particles, increasing the evolution speed and preventing local optimum.And the Matlab graphics displaying function, realizing the dynamic visual iteration process is optimized.Finally by the instance analysis, it efficiently gets the optimal scheduling scheme, which realizes the application of the hybrid PSO algorithm in the military equipment resource scheduling problems.
作者
沈延安
叶霖
SHEN Yan-an;YE Lin(Army Officer Academy, Hefei 230031,China)
出处
《火力与指挥控制》
CSCD
北大核心
2019年第1期6-11,共6页
Fire Control & Command Control
基金
安徽省自然科学基金资助项目(1508085MF131)
关键词
任务调度
混合粒子群算法
浓度监控
维修保障
无人机
可视化
task scheduling
hybrid particle swarm optimization (HPSO) algorithm
concentration monitoring
maintenance support
unmanned aerial vehicle(UAV)
visualization
作者简介
沈延安(1978- ),男,安徽寿县人,副教授,博士。研究方向:管理系统优化与决策研究;通信作者:叶 霖(1992- ),男,河南焦作人,在读研究生。研究方向:管理信息系统。
