基于量子蚁群算法的网格任务调度研究

任务调度策略是网格计算的核心问题。在系统任务调度和资源分配中,提出一种基于量子蚁群算法的任务调度策略。算法将量子计算与蚁群算法相融合,通过对蚁群进行量子化编码并采用量子旋转门及非门操作,实现对任务自适应启发式的分配和优化。算法有效增强了种群的多样性、克服了遗传算法和蚁群算法的早熟收敛和退化现象。仿真实验中,分别与基于遗传算法和基于蚁群算法的任务调度策略相对比,结果表明算法有效缩短了任务调度的时间跨度,增强了网格系统的性能。

Research of grid task schedule based on quantum ant colony algorithm

Task schedule strategy is the key issue of grid computing.During the schedule and allocation of the system tasks, task schedule strategy based on quantum ant colony algorithm is proposed.This algorithm combines quantum computing with the ant colony algorithm and achieves optimal task schedule by quantum coding and quantum evolution operator.It ensures the diversity of population and overcomes premature convergence and degradation of the genetic algorithm and ant colony algorithm.Compared with the genetic algorithm and ant colony algorithm task schedule strategy, simulations show that the search ability of this algorithm is better, and it can reduce the time span of the task schedule and enhance the performance of grid system effectively.