ACOGA算法的多媒体网络QoS路由实现

崔梦天; 钟勇; 赵海军

doi:10.3969/j.issn.1001-0548.2009.02.26

ACOGA算法的多媒体网络QoS路由实现

1.
中国科学院成都计算机应用研究所成都 610041;
2.
西华师范大学计算机学院四川南充 637002;
3.
四川邮电职业技术学院计算机科学系成都 610067

基金项目:

四川省科技攻关项目(07GG006-014)

详细信息

作者简介:
崔梦天(1972-)，女，博士生，副教授，主要从事软件理论方面的研究.

中图分类号: TN915.1
计量
- 文章访问数: 4742
- HTML全文浏览量: 166
- PDF下载量: 80
出版历程
- 收稿日期: 2008-10-07
- 修回日期: 2009-01-17
- 刊出日期: 2009-04-14

Realization to Multimedia Network QoS Routing Based on ACOGA

1.
Chengdu Institute of Computer Applications,Chinese Academy of Science Chengdu 610041;
2.
School of Computer Science,China-West Normal University Nanchong Sichuan 637002;
3.
Department of Computer Science,Sichuan Post and Communication College Chengdu 610067

摘要

摘要: 针对传统的路由算法收敛速度慢且容易产生拥塞和路由振荡问题，提出了基于蚁群算法(ACO)和遗传算法(GAs)来实现动态QoS路由的新算法。分析了基本的ACO的正反馈性、协同性、并行性和鲁棒性等优点，同时利用GAs很强的自适应性和种群优化技术，通过对ACO算法使用遗传算法的交叉、变异达到对信息素进行调整，来自适应地调整路径选择概率的确定策略和信息量更新策略，从而扩大搜索范围。计算和仿真结果表明，该方法具有更好的路由收敛速度和稳定性，能更有效地解决拥塞现象和路由振荡问题。
- 蚁群算法 /
- 遗传算法 /
- 基于路由的服务质量 /
- 信息素
Abstract: To solve the problem of low convergence speed and congestion and oscillation in conventional routing algorithms, a novel method of dynamic routing algorithm for multimedia network is proposed based on ant colony optimization (ACO) algorithm and genetic algorithms (GAs). The essential advantages of ACO including cooperation, positive feedback, and distributed nature and the disadvantages of low convergence speed are discussed. By considering the high adaptability of GAs, the cross operation and mutation of genetic algorithms are introduced into the ACO to improve its searching ability and to dynamically adjust the influence of each ant for the trail information updating and the selected probabilities of the paths. The algorithm is also well suited for dynamic networks and can make the selected paths shortest, miss the traffic jams and keep the balance of networks load distribution.
- ant colony optimization /
- genetic algorithms /
- QoS-based routing /
- trail information