基于最优学习的随机网络优化算法  

The Stochastic Network Optimization Algorithm Based on Optimal Learning

作　　者：周素青[1] ZHOU Su-qing(Department of Computer Engineering,Fujian Polytechnic of Information Technology,Fuzhou 350003,China)

机构地区：[1]福建信息职业技术学院计算机工程系

出　　处：《西安文理学院学报（自然科学版）》2019年第6期76-79,共4页Journal of Xi’an University(Natural Science Edition)

基　　金：福建省中青年教师教育科研项目(JAT160735)

摘　　要：为了应对动态的网络环境,设计了基于最优学习的网络队列控制(Optimal Learning based Queuing Control,OLQC)算法.OLQC是一种在线算法,能够感知环境的变化,并自行学习最优的队列控制策略.仿真实验结果表明,OLQC算法能够以较低的能耗来维持网络的稳定性,并能自适应地应对网络的变化.In order to cope with the dynamic network environment, an optimal learning based Queuing Control(OLQC) algorithm is designed. OLQC is an online algorithm, which can sense the changes of the environment and learn the optimal queue control strategy by itself. The simulation results show that the OLQC algorithm can maintain the stability of the network with low energy consumption and adapt to the changes of the network.

关 键 词：最优学习 队列控制 在线算法 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]