计算机与现代化

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基于改进蚁群优化算法的网络服务质量路由算法

  

  1. 河南工业职业技术学院计算机工程系,河南南阳473000
  • 收稿日期:2015-02-05 出版日期:2015-07-23 发布日期:2015-07-28
  • 作者简介:马世欢(1976-),男,河南南阳人,河南工业职业技术学院计算机工程系讲师,硕士,研究方向:计算机应用技术; 李伟(1982-),男,河南南阳人,助教,硕士,研究方向:计算机应用 技术。

Routing Algorithm for Quality of Service Based on Improved #br# Ant Colony Optimization Algorithm

  1. Department of Computer Engineering, Henan Polytechnic Institute, Nanyang 473000, China
  • Received:2015-02-05 Online:2015-07-23 Published:2015-07-28

摘要:

针对当前无线网络路由算法存在丢包率高、节点拥塞严重的难题,提出一种基于改进蚁群优化算法的网络服务质量路由算法。首先根据无线网络的特点选择带宽、端到端的延迟、数据包丢失率以
及链路花费作为QoS参数,并建立一个多约束网络服务质量路由优化问题的数学模型,然后采用具有正反馈机制和搜索能力强的蚁群优化算法对数学模型进行求解,并根据无线网络路由特点对标准蚁群优
化算法进行改进,提高其搜索性能,最后采用具体仿真实验对路由算法的性能进行测试。实验结果表明,改进蚁群优化算法在满足网络质量要求的条件下,不仅降低了网络平均延时,而且减少了网络数
据丢包率,性能优于其它算法。

关键词: 无线网络, 路由算法, 蚁群优化算法, 服务质量参数

Abstract:

The traditional wireless network routing algorithms have high packet loss rate and serious node congestion problems, so this paper proposes a routing algorithm for
quality of service based on improved ant colony optimization algorithm. Firstly, according to the wireless network characteristics, the bandwidth, endtoend delay, packet
loss rate and the link cost are chosen as the QoS parameters, and a mathematical model for network routing optimization problem with multiconstraint quality of service is 
established, and then the ant colony optimization algorithm which has positive feedback mechanism and the search ability is used to solve the mathematical model, and the
standard ant colony optimization algorithm is improved to promote search performance according to the routing characteristics of wireless network, finally the performance of the
routing algorithm is tested by simulation experiment. The experimental results show that, the improved ant colony optimization algorithm can satisfy the quality requirements for
wireless network, not only reduces the average network delay and the network data packet loss rate, and performance is better than other routing algorithms.

Key words: wireless network; routing algorithm; ant colony optimization algorithm; quality of service&rsquo, s parameters

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