计算机科学 ›› 2019, Vol. 46 ›› Issue (5): 290-297.doi: 10.11896/j.issn.1002-137X.2019.05.045
简琤峰, 况祥, 张美玉
JIAN Cheng-feng, KUANG Xiang, ZHANG Mei-yu
摘要: 针对传统的云计算调度模型对任务调度求解时间长的缺陷,提出一种结合差分进化的改进的新蝙蝠算法(Optimized Novel Bat Algorithm,ONBA)优化算法来获取任务的调度数据。利用该调度数据对改进的改进的深度信念网络(Improved Deep Belief Network,IDBN)模型进行训练,通过对训练学习率和训练次数的自适应调优来实现训练时效的提高,从而实现对云计算调度结果的快速准确预测。实验结果表明,应用该方法训练完成的改进IDBN模型进行调度时,在保证预测群组优化结果准确的前提下,其能够有效缩短云计算的实际调度时间,弥补了传统群组优化模型调度耗时的缺陷。
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