A Genetic Algorithm-Ant Colony Algorithm(GA-ACA),which can be used to optimize multi-Unit Under Test(UUT)parallel test tasks sequences and resources configuration quickly and accurately,is proposed in the paper.With t...A Genetic Algorithm-Ant Colony Algorithm(GA-ACA),which can be used to optimize multi-Unit Under Test(UUT)parallel test tasks sequences and resources configuration quickly and accurately,is proposed in the paper.With the establishment of the mathematic model of multi-UUT parallel test tasks and resources,the condition of multi-UUT resources mergence is analyzed to obtain minimum resource requirement under minimum test time.The definition of cost efficiency is put forward,followed by the design of gene coding and path selection project,which can satisfy multi-UUT parallel test tasks scheduling.At the threshold of the algorithm,GA is adopted to provide initial pheromone for ACA,and then dual-convergence pheromone feedback mode is applied in ACA to avoid local optimization and parameters dependence.The practical application proves that the algorithm has a remarkable effect on solving the problems of multi-UUT parallel test tasks scheduling and resources configuration.展开更多
针对多UUT(Unit Under Test)并行测试任务调度与资源配置问题,提出了一种遗传蚁群融合算法.应用遗传蚁群融合算法能快速、准确地寻找到具有最大成本效率的多UUT并行测试资源配置和任务序列.建立了多UUT并行测试任务资源描述的数学模型,...针对多UUT(Unit Under Test)并行测试任务调度与资源配置问题,提出了一种遗传蚁群融合算法.应用遗传蚁群融合算法能快速、准确地寻找到具有最大成本效率的多UUT并行测试资源配置和任务序列.建立了多UUT并行测试任务资源描述的数学模型,分析了多UUT测控资源合并的条件,得出最短并行测试时间基础上的最少资源需求,给出了成本效率的定义,设计了一种满足多UUT并行测试任务调度的基因编码方法和路径选择方案.算法初期利用遗传算法的快速收敛性,为蚁群算法提供初始信息素分布,蚁群算法采用双向收敛的信息素反馈方式,避免了对参数的依赖,减少了局部收敛性,加快了收敛速度.实例表明,该算法能很好地解决多UUT任务资源最优调度与配置问题.展开更多
基金supported by“11th Five-year Projects”pre-research projects fund of the National Arming Department
文摘A Genetic Algorithm-Ant Colony Algorithm(GA-ACA),which can be used to optimize multi-Unit Under Test(UUT)parallel test tasks sequences and resources configuration quickly and accurately,is proposed in the paper.With the establishment of the mathematic model of multi-UUT parallel test tasks and resources,the condition of multi-UUT resources mergence is analyzed to obtain minimum resource requirement under minimum test time.The definition of cost efficiency is put forward,followed by the design of gene coding and path selection project,which can satisfy multi-UUT parallel test tasks scheduling.At the threshold of the algorithm,GA is adopted to provide initial pheromone for ACA,and then dual-convergence pheromone feedback mode is applied in ACA to avoid local optimization and parameters dependence.The practical application proves that the algorithm has a remarkable effect on solving the problems of multi-UUT parallel test tasks scheduling and resources configuration.
文摘针对多UUT(Unit Under Test)并行测试任务调度与资源配置问题,提出了一种遗传蚁群融合算法.应用遗传蚁群融合算法能快速、准确地寻找到具有最大成本效率的多UUT并行测试资源配置和任务序列.建立了多UUT并行测试任务资源描述的数学模型,分析了多UUT测控资源合并的条件,得出最短并行测试时间基础上的最少资源需求,给出了成本效率的定义,设计了一种满足多UUT并行测试任务调度的基因编码方法和路径选择方案.算法初期利用遗传算法的快速收敛性,为蚁群算法提供初始信息素分布,蚁群算法采用双向收敛的信息素反馈方式,避免了对参数的依赖,减少了局部收敛性,加快了收敛速度.实例表明,该算法能很好地解决多UUT任务资源最优调度与配置问题.
