基于改进自适应遗传算法的并行测试任务调度. (Chinese)

Aiming at the problem that parallel test task scheduling needs to avoid resource competition, system deadlock and starvation, which makes optimization of scheduling solutions difficult, a task scheduling algorithm based on an improved adaptive genetic algorithm is proposed. This algorithm designs a population dissimilarity function as Criteria for evaluating population diversity, and adaptively adjusting crossover and mutation probabilities based on population dissimilarity to ensure population diversity during the entire iteration process. Comparison of test results and algorithms in an automatic testing system shows that this algorithm can effectively solve Parallel testing of task scheduling problems can reduce the possibility of falling into a local optimal solution, improve the efficiency and accuracy of the algorithm's search for optimal solutions, and achieve better search performance. [ABSTRACT FROM AUTHOR]

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-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. [ABSTRACT FROM AUTHOR]

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