面向系统的服务量预测算法①
周子馨
(河海大学 计算机与信息学院, 南京 211100)
通讯作者: 周子馨, E-mail: ***************
摘　要: 系统的服务量受到多种因素影响, 为提高系统的服务量预测精度, 本文基于离散变异控
制参数的自适应差分进化算法DMPSADE, 提出了一种改进算法IDMPSADE, 并将其与长短时记忆神经网络LSTM 相结合建立了对服务量的预测模型IDMPSADE-LSTM. 在IDMPSADE 中, 当子代种测试函数寻优性能没有父代种好时, 对父代种个体进行反向引导, 跳出局部最优, 提升搜索到全局最优能力. 由于LSTM 的神经元数量、迭代次数、学习率以及训练批次需要通过经验进行设置, 具有较大的随机性, 故利用IDMPSADE 对这些参数进行寻优.IDMPSADE-LSTM 将分析得到的气温、降水量作为影响因素结合服务量的时间特征对系统的服务量进行预测. 文中实验结果表明, IDMPSADE-LSTM 预测模型比一般的神经网络以及SARIMA-SVM 混合预测模型的精确度要高.关键词: 系统服务量; 时间序列预测; 差分进化算法; 长短时记忆神经网络
引用格式:  周子馨.面向系统的服务量预测算法.计算机系统应用,2020,29(4):137–143. /1003-3254/7337.html Service Volume Prediction Algorithm for Online Customer Service System
ZHOU Zi-Xin
(College of Computer and Information, Hohai University, Nanjing 211100, China)
Abstract : The service volume of online customer service system is affected by many factors. In order to improve the prediction accuracy of service volume, an improved algorithm IDMPSADE is propose
d on the basis of self-adaptive differential evolution algorithm DMPSADE with discrete mutation control parameters. By combining IDMPSADE with Long-Short Term Memory network (LSTM), an IDMPSADE-LSTM prediction model of service volume is established.IDMPSADE chooses the reverse guidance of the parent population whose child population’s performance on test functions is not as good as it, which can escape from the local optimum and improve the capability of searching the optimal solution within defined space. LSTM’s parameters, such as number of neurons, epochs, learning rate, and batch-size, are set by experience and have larger randomness, and IDMPSADE could be helpful to optimize these parameters.IDMPSADE-LSTM prediction model uses temperature and precipitation as influencing factors and combines with the temporal characteristics of service volume to predict the service volume. The experimental results show that the proposed IDMPSADE-LSTM prediction model is more accurate compared with general neural networks and SARIMA-SVM hybrid prediction model.
网上系统软件
Key words : service volume of online customer service system; time series prediction; differential evolution algorithm; Long-Short Term Memory (LSTM)
计算机系统应用 ISSN 1003-3254, CODEN CSAOBN
E-mail: ************ Computer Systems & Applications,2020,29(4):137−143 [doi: 10.15888/jki.csa.007337]
©中国科学院软件研究所版权所有.Tel: +86-10-62661041① 基金项目: 国网江苏省电力有限公司科技项目(J2018020)
Foundation item: Science and Technology Project of State Grid Jiangsu Electric Power Co. Ltd. (J2018020)
收稿时间: 2019-08-22; 修改时间: 2019-09-09, 2019-09-19; 采用时间: 2019-09-23; csa 在线出版时间: 2020-04-05
Software Technique•Algorithm 软件技术•算法 137

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