计算机工程与应用2018，Vol.54Issue(13)：73-77,130,6.DOI:10.3778/j.issn.1002-8331.1705-0137
改进鲸鱼算法在云计算资源负载预测中的应用
Application of improved whale algorithm in load forecasting of cloud computing resources
摘要
Abstract
In order to solve the problem caused by the traditional load forecasting of cloud computing resources method’s low prediction accuracy of sequence high frequency components and weak generalization ability, this paper proposes a method of combining wavelet packet decomposition and Whale Optimization Algorithm based on Chaotic Sine Cosine operator(CSCWOA)to optimize the MultiLayer Perceptron(MLP)neural network for short-term cloud computing resources forecasting. Load sequence is decomposed into multiple band series by wavelet packet. While the CSCWOA-MLP optimized neural network is enrolled to predict the sub-band series. And by superimposing and reconstructing all the forecasting series, it gets the actual forecasting results. The experimental results show that the method can grasp the change rule of the impact burr perfectly, which has good prediction accuracy and generalization ability.关键词
云计算资源负载预测/正余混沌双弦鲸鱼优化算法/小波包变换/多层感知器神经网络Key words
load forecasting of cloud computing resources/whale optimization algorithm based on chaotic sine cosine operator/wavelet packet transform/multilayer perceptron neural network分类
信息技术与安全科学引用本文复制引用
谢建群,刘怡俊,李生..改进鲸鱼算法在云计算资源负载预测中的应用[J].计算机工程与应用,2018,54(13):73-77,130,6.基金项目
国家自然科学基金(No.61106019) （No.61106019）
广东省和广州市科技计划项目(No.2015B090901060,No.2016B090903001,No.2016B090904001,No.2016B090918126,No.201604010051). （No.2015B090901060,No.2016B090903001,No.2016B090904001,No.2016B090918126,No.201604010051）
