基于GSPN和人工免疫算法的并行测试任务调度研究

针对ATS中并行测试任务调度复杂、难以优化的问题,提出了一种广义随机Petri网和人工免疫算法相结合的任务调度优化算法.首先对并行测试系统建立广义随机Petri网(GSPN)模型,然后将激发的变迁序列集作为并行测试任务调度路径;将免疫克隆选择算法(ICSA)应用到并行测试系统任务调度问题中,并提出一种自适应克隆选择算子,搜索最优任务调度路径,得到以测试时间最短为目标的最优任务调度方案.用某型雷达接收机并行测试系统对该算法进行仿真验证,结果表明,与改进的混合遗传算法(IHGA)相比,该算法能够便捷地得到任务调度最优序列,且测试效率更高.

Research on Task Scheduling for Parallel Test System Based on GSPN and Artificial Immune Algorithm

In order to solve the problems of complex task scheduling and difficult optimization in automatic test system,a task scheduling optimization algorithm based on generalized stochastic Petri nets (GSPN) and artificial immune algorithm is proposed.At first,a GSPN model for the parallel test system is established,then the fired transition sequence is used as the task scheduling path of parallel test.The immune clone selection algorithm (ICSA) is used to solve the problem of task scheduling in parallel test system.In order to get the optimal scheduling scheme with shortest time,an adaptive clone selection operator is put forward to search the optimal task scheduling path.The proposed algorithm is verified in certain parallel test system for radar.Simulation results show that compared with the improved hybrid genetic algorithm (IHGA),this algorithm can find the optimal task scheduling sequence more easily and test more efficiently.