基于改进自适应遗传算法的并行测试任务调度

Parallel test task scheduling based on improved adaptive genetic algorithm

针对并行测试任务调度需要避免资源竞争、系统死锁与饿死,导致调度方案优化困难的问题,提出了一种基于改进自适应遗传算法的任务调度算法。该算法设计了种群相异度函数作为评价种群多样性的标准,并根据种群相异度自适应调节交叉与变异概率以保证整个迭代过程中种群的多样性。在某自动测试系统中的测试结果和算法对比表明,该算法可以有效解决并行测试任务调度问题,能够减小陷入局部最优解的可能性,提高算法搜索最优解的效率与准确性,实现较好的搜索性能。

A task scheduling algorithm based on improved genetic algorithm is proposed to solve the problem that parallel test task scheduling needs to avoid resource competition,system deadlock and starvation,which makes it difficult to optimize the scheduling scheme.The algorithm adopts the population dissimilarity function as the standard to evaluate the population diversity,and adaptively adjusts the crossover and mutation probability according to the population dissimilarity to ensure the population diversity in the whole iteration process.The test results and algorithm comparison in an automatic test system show that the algorithm can effectively solve the parallel test task scheduling problem,reduce the possibility of falling into the local optimal solution,improve the efficiency and accuracy of the algorithm to search the optimal solution,and achieve better search performance.