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

作　　者：姜瑞 韩尧 张大为 JIANG Rui;HAN Yao;ZHANG Dawei(School of Aeronautics and Astronautics,University of Electronic Science and Technology of China,Chengdu 611731,China)

机构地区：[1]电子科技大学航空航天学院,成都611731

出　　处：《兵器装备工程学报》2023年第9期298-305,共8页Journal of Ordnance Equipment Engineering

基　　金：四川省科技计划项目(2021YJ0099)。

摘　　要：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.

关 键 词：自动测试 并行测试 任务调度 遗传算法 自适应 

分 类 号：TP202[自动化与计算机技术—检测技术与自动化装置]