多元宇宙优化算法优化WLSSVM的网络流量预测  

Multiverse optimization algorithm optimizes network traffic prediction of weighted least squares support vector machine

作　　者：雷晓星[1] LEI Xiaoxing(Guangzhou Vocational College of Technology&Business,Guangzhou 511442,China)

机构地区：[1]广州科技贸易职业学院,广东广州511442

出　　处：《自动化与仪器仪表》2023年第5期51-55,共5页Automation & Instrumentation

基　　金：广东省教育厅,《基于大数据的实验室智能运维管理研究》项目编号(2020KTSCX291)。

摘　　要：高精度网络流量预测可以帮助管理人员了解网络流量变化态势,提高网络系统的稳定性,为了降低网络流量预测的误差,提出了基于多元宇宙优化算法优化加权最小二乘支持向量机的网络流量预测模型。首先采用网络流量历史数据,将其作为加权最小二乘支持向量机的输入向量,然后利用多元宇宙优化算法对加权最小二乘支持向量机参数寻优,从而得到最优的网络流量预测模型,最后采用具体网络流量预测应用实例对模型性能进行测试与分析,结果表明本模型可以准确描述网络流量的变化规律,预测误差很小,完全能够满足网络管理实际要求,相对于其他预测模型,本模型的网络流量预测精度得到了有效提高。High precision network traffic prediction can help managers understand the changing trend of network traffic and improve the stability of network system.In order to reduce the error of network traffic prediction,a network traffic prediction model based on multiuniverse optimization algorithm and weighted least squares support vector machine is proposed.Firstly,the network traffic history data is used as the input vector of the weighted least squares support vector machine,and then the multi universe optimization algorithm is used to find the optimal solution for the parameters of the weighted least squares support vector machine,so as to construct the optimal network traffic prediction model.Finally,a specific network traffic prediction application example is used to test and analyze the performance of the model,This model can accurately describe the change law of network traffic.The network traffic prediction error is very small,which fully meets the actual requirements of network management.Compared with other prediction models,the network traffic prediction accuracy of this model has been effectively improved.

关 键 词：多元宇宙优化算法 网络流量 预测模型 最优参数 加权最小二乘支持向量机 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]