基于蚁群算法的Qo S路由模型的设计与优化
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摘要
为了提升网络数据传输中路由性能,采用蚁群算法解决网络路由出现的问题。首先介绍了蚁群算法的原理与模型,然后根据实际应用的QoS路由具体问题,建立QoS路由模型,设计基于ACO的Qo S路由算法。通过仿真实验,对参数进行优化分析,验证了蚁群算法在网络路由中的应用效果,证明这种方法能够提高网络数据传输中路由的性能。
Ant colony algorithm is used to solve network routing problem and to enhance routing performance in network data transmission. Firstly,the principles and models of ant colony algorithm are introduced. Secondly,the practical application of QoS routing problems,building QoS routing model,QoS routing algorithm is designed. Through simulation experiments,the opitmization analysis of the parameters has been made. The study has verified the application effect of ant colony algorithm in network routing and proved that the algorithm can improve the performance of routing in network data transmission.
Ant colony algorithm is used to solve network routing problem and to enhance routing performance in network data transmission. Firstly,the principles and models of ant colony algorithm are introduced. Secondly,the practical application of QoS routing problems,building QoS routing model,QoS routing algorithm is designed. Through simulation experiments,the opitmization analysis of the parameters has been made. The study has verified the application effect of ant colony algorithm in network routing and proved that the algorithm can improve the performance of routing in network data transmission.
引文
[1]肖艳秋,焦建强,乔东平,等.蚁群算法的基本原理及应用综述[J].轻工科技,2018,34(3):69-72.
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[8]孔祥彬,沈苏彬,李莉.一种基于SDN网络的Qo S路由选择方案[J].计算机技术与发展,2018,28(2):102-106.
[9]杨再甫,黄友锐,曲立国,等. TSP的改进蚁群算法求解及其仿真研究[J].合肥工业大学学报(自然科学版),2014,37(8):928-932.
[10] STUTZLE T,HOOS H. MAX-MIN Ant System and Local Search for the Traveling Salesman Problem[C]. IEEE International Conference on Evolutionary Computation,2002:309-314.
[11]朱虹.网络设备虚拟化下路由选择问题探讨[J].金融科技时代,2018(7):77-78.
[12]王军.路由选择协议的合理选择[J].上海铁道大学学报,2000,21(8):85-89.
[2] DORIGO M,STTZLE T. The ant colony optimization metaheuristic:Algorthms,application,and advances[J]. International,2001,57(3):251-285.
[3] GUTJAHR W J. ACO algorithms with guaranteed convergence to the optimal solution[J]. Information Processing Letters,2002,82(3):145-153.
[4]罗回彬,黄嘉爔,卢学文.根据改进蚁群算法的楼宇疏散系统设计[J].单片机与嵌入式系统应用,2018,18(7):44-47.
[5]鲍文杰,朱信忠,赵建民,等.加权值多态蚁群算法[J].软件工程,2016,19(4):1-4.
[6]蔡海雄,陈峰震.新型蚁群算法的分析与研究[J].福建电脑,2018,34(6):19-20.
[7]杜衡吉,李勇.蚁群算法中参数设置对其性能影响的研究[J].现代计算机(专业版),2012(13):3-7.
[8]孔祥彬,沈苏彬,李莉.一种基于SDN网络的Qo S路由选择方案[J].计算机技术与发展,2018,28(2):102-106.
[9]杨再甫,黄友锐,曲立国,等. TSP的改进蚁群算法求解及其仿真研究[J].合肥工业大学学报(自然科学版),2014,37(8):928-932.
[10] STUTZLE T,HOOS H. MAX-MIN Ant System and Local Search for the Traveling Salesman Problem[C]. IEEE International Conference on Evolutionary Computation,2002:309-314.
[11]朱虹.网络设备虚拟化下路由选择问题探讨[J].金融科技时代,2018(7):77-78.
[12]王军.路由选择协议的合理选择[J].上海铁道大学学报,2000,21(8):85-89.