摘要
为解决航空电子设备在进行大规模多线程自动测试任务时任务调度效率低、资源利用率低的问题,设计负载均衡筛选机制,建立了并行测试静态资源调度模型,提出了基于粒子编解码的改进粒子群任务调度方法。通过混沌初始化序列和决策权重选择双向学习或引斥力机制,提高了粒子群算法效率和准确性。在此基础上,针对自动测试中的重调度问题,以待测件紧急度为判据建立不同目标函数,完成测试过程紧急件的测试,提高了调度方法的动态规划能力。通过仿真实验验证了调度方法能够有效提升并行测试任务调度效率和资源利用率。
To solve the problem of low scheduling efficiency and resource utilization rate of large-scale multithreaded testing tasks in automatic test of avionic equipment a load balancing screening mechanism is designed a parallel testing static resource scheduling model is established and an improved Particle Swarm Optimization(PSO)task scheduling algorithm based on particle encoding and decoding is proposed.Bidirectional learning or gravitational/repulsive force mechanism is selected through chaotic initialization sequence and decision weight selection thus the efficiency and accuracy of PSO is improved.On this basis in response to the rescheduling problem in automatic testing different objective functions are established based on the urgency of the test piece to complete the test of urgent parts during the testing process and thus the dynamic planning ability of the scheduling method is improved.Simulation experiments have verified that the scheduling method can effectively improve the scheduling efficiency and resource utilization rate of parallel testing tasks.
作者
王娟
郑超
崔海青
刘哲旭
WANG Juan;ZHENG Chao;CUI Haiqing;LIU Zhexu(Civil Aviation University of China Engineering Technology Training Center,Tianjin 300000 China;Civil Aviation University of China School of Electronic Information and Automation,Tianjin 300000 China)
出处
《电光与控制》
CSCD
北大核心
2024年第11期90-95,108,共7页
Electronics Optics & Control
基金
天津市多元投入基金项目(21JCQNJC00710)。
关键词
航空电子设备
并行测试
任务调度
粒子群算法
重调度
avionic equipment
parallel testing
task scheduling
particle swarm optimization
rescheduling
