基于改进GA-PSO的可重构测试资源匹配方法
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摘要
为提高测点信号与可重构测试资源匹配效率,建立了基于STD标准的测点信号与可重构测试资源的数学描述模型。针对可重构测试资源的特点,结合工程实际提出了基于Sigmoid函数的匹配函数,以资源可靠性、配置文件大小及配置时间因子作为罚函数,利用匹配函数构造出遗传算法的适应度函数。为解决遗传算法搜索速度较慢的问题,改进了遗传算法的选择算子和交叉算子,将粒子群算法应用到遗传算法中,解决了遗传算法在算法后期迭代效率低下的问题,最后通过实例验证了算法的有效性。
In order to improve the matching efficiency between test point signal and reconfigurable test resource, a mathematical description model based on STD standard for test point signal and reconfigurable test resource is established. According to the characteristics of reconfigurable test resources, a matching function based on Sigmoid function was proposed in combination with engineering practice. Additionally, the fitness function of the genetic algorithm was constructed by using the matching function, taking the reliability of the resource, the size of the configuration file and the time factor as penalty function. The selection operator and crossover operator of genetic algorithm were improved to tackle the problem of slow search speed of genetic algorithm, the particle swarm algorithm is applied to the genetic algorithm, which solves the problem of the low iterative efficiency of genetic algorithm in the late algorithm. Finally, the validity of the algorithm is verified by an example.
In order to improve the matching efficiency between test point signal and reconfigurable test resource, a mathematical description model based on STD standard for test point signal and reconfigurable test resource is established. According to the characteristics of reconfigurable test resources, a matching function based on Sigmoid function was proposed in combination with engineering practice. Additionally, the fitness function of the genetic algorithm was constructed by using the matching function, taking the reliability of the resource, the size of the configuration file and the time factor as penalty function. The selection operator and crossover operator of genetic algorithm were improved to tackle the problem of slow search speed of genetic algorithm, the particle swarm algorithm is applied to the genetic algorithm, which solves the problem of the low iterative efficiency of genetic algorithm in the late algorithm. Finally, the validity of the algorithm is verified by an example.
引文
[1]付新华,肖明清,基于一种匹配函数的ATS资源自动配置方法[J].北京航空航天大学学报,2008,34(12):1392-1397.
[2]朱海振,肖明清,赵鑫,等.军用自动测试设备更新决策研究[J].空军工程大学学报(自然科学版),2017,18(4):53-59.
[3]李世斌,端子功能可配置ATE仪器资源模块研制[D].哈尔滨:哈尔滨工业大学,2015.
[4]Chang Y,Wang z G.Design of th e reconfigurable instrument based on configuration principle[C]//2010 2nd International Conference on Industrial and Information Systems.2010:409-412.
[5]Gao c J,Sheng S,Zhao L L.Reconfigurable intelligent instrument based on FPGA[J].Applied Mechanics and Materials,2010,44-47:767-771.
[6]周越文,付新华,孔庆春,等,弹性资源下的测试系统资源配置方[J].测控技术,2010,29(3):18-22.
[7]丁超,唐力伟,邓士杰.基于匹配函数的云测试资源匹配算法研究[J].仪器仪表学报,2015,36(12):2697-2705.
[8]付振鹏.面向导弹测试的PXI可重构仪器研究[D].哈尔滨:哈尔滨工业大学,2014.
[9]Chiang c W.Two novel genetic operators for task matching and scheduling in heterogeneous computing environments[J].Journal of Intemet Technology,2012,13(5):773-784.
[10]张卫祥,齐玉华,李德治,基于离散粒子群算法的测试用例优先排序[J].计算机应用,2017,37(1):108-113.
[11]郑直,范惠林,张司明,基于改进离散粒子群禁忌算法的并行测试任务调度[J].测控技术,2014,33(9):143-145.
[12]石利平.基于SA的改进遗传算法的测试数据生成研究[J].测控技术,2013,32(7):114-117.
[13]边泽强,孟晓风,陈粤.基于多色蚁群的柔性测试系统测试资源匹配[J].测试技术学报,2007,21(6):488-492.
[2]朱海振,肖明清,赵鑫,等.军用自动测试设备更新决策研究[J].空军工程大学学报(自然科学版),2017,18(4):53-59.
[3]李世斌,端子功能可配置ATE仪器资源模块研制[D].哈尔滨:哈尔滨工业大学,2015.
[4]Chang Y,Wang z G.Design of th e reconfigurable instrument based on configuration principle[C]//2010 2nd International Conference on Industrial and Information Systems.2010:409-412.
[5]Gao c J,Sheng S,Zhao L L.Reconfigurable intelligent instrument based on FPGA[J].Applied Mechanics and Materials,2010,44-47:767-771.
[6]周越文,付新华,孔庆春,等,弹性资源下的测试系统资源配置方[J].测控技术,2010,29(3):18-22.
[7]丁超,唐力伟,邓士杰.基于匹配函数的云测试资源匹配算法研究[J].仪器仪表学报,2015,36(12):2697-2705.
[8]付振鹏.面向导弹测试的PXI可重构仪器研究[D].哈尔滨:哈尔滨工业大学,2014.
[9]Chiang c W.Two novel genetic operators for task matching and scheduling in heterogeneous computing environments[J].Journal of Intemet Technology,2012,13(5):773-784.
[10]张卫祥,齐玉华,李德治,基于离散粒子群算法的测试用例优先排序[J].计算机应用,2017,37(1):108-113.
[11]郑直,范惠林,张司明,基于改进离散粒子群禁忌算法的并行测试任务调度[J].测控技术,2014,33(9):143-145.
[12]石利平.基于SA的改进遗传算法的测试数据生成研究[J].测控技术,2013,32(7):114-117.
[13]边泽强,孟晓风,陈粤.基于多色蚁群的柔性测试系统测试资源匹配[J].测试技术学报,2007,21(6):488-492.