基于改进DPSO算法的并行测试任务优化调度研究

并行测试以减少测试时间和降低测试成本的强大优势,已成为当前自动测试系统发展的方向。针对并行自动测试过程中,测试任务调度复杂,难以优化的问题,以PSO算法为基础,通过对问题空间编码的重新定义,并运用交叉、变异算子给出了新的粒子位置的更新公式,提出了一种改进后的DPSO算法。依据并行测试完成时间极限定理,给出了并行测试任务调度的目标函数与约束条件。以某雷达电子装备并行测试系统中三块电路板并行测试为例,对改进的DPSO算法进行了仿真验证,得到了最优调度测试序列。结果表明：与遗传算法相比,改进后的DPSO算法迭代次数更少,寻优性能更好,适用于工程应用。

Research on optimize Task Scheduling for Parallel TestBased on Improved DPSO Algorithm

The parallel test has become the developmental trend of Automatic Test System with great strength in reducing test time and test cost. Aimed at the problems that task scheduling is complex and task optimization is difficulty in parallel automatic test, a improved Discrete Particle Swarm Optimization (DPSO) algorithm is proposed, in which problem space coding is redefined and particle position update formula is rebuilt using crossover and mutation operator. And then the objective function and constraint condition of task scheduling for parallel test are given, according to the limit completion time theorem of parallel test. In order to validate the performance of the improved DPSO algorithm, a parallel test simulation experiment for three pieces of circuit board is made by parallel test system of certain radar electronic equipment, and the optimal task scheduling is got. The results show that compared with genetic algorithm the improved DPSO algorithm has less iterations, higher efficiency and better optimal performance, and is more suitable for engineering application.