一种基于云蚁群算法优化的QoS多播路由研究

发布时间：2018-03-22 14:21

本文选题：多播路由　切入点：云　出处：《辽宁科技大学》2014年硕士论文　论文类型：学位论文

【摘要】：随着信息时代的高速发展，网络生活已经完全融入到人们的日常生活中，那么在适应这种生活模式的同时，人们也在寻求更加舒适的体验和享受，QoS作为一种衡量标准应运而生。对于在QoS基础上寻求使用最小花费来获取最大的网络资源已经成为网络研究的一个重大方向。本文针对蚁群算法自身的缺陷，在分析了蚁群算法在解决路由问题中存在的路由选择时间长，全局收敛能力差和容易陷入局部最优等问题，对原有的基本蚁群算法使用云模型进行优化，提高了算法效率。论文采用QoS多播路由模型进行模拟，在论文中间部分还对其相关知识进行叙述，主要包括多播相关技术、QoS约束，QoS算法的现状等。论文采用无线路由来构建多播路由算法的数学模型，结合蚁群算法和云模型来优化网络开销，通过最后的数据验证，从而得出算法在理论上的可行性。由于蚂蚁群体的正反馈机制，导致算法容易陷入局部最优，论文改进了信息素挥发策略，，使用云模型作动态自适应规划，动态地调整局部信息素更新策略，提高了算法的有效性。在整体信息素更新策略上采用最新的最优最差路径更新规则，提高了算法的全局收敛性。仿真结果表明：CACA(云蚁群算法)在解决路由问题上是有效的，和传统的QoS多播路由算法相比在收敛性、收敛速度上都有很大的提高，代价树也得到优化。
[Abstract]:With the rapid development of the information age, network life has been fully integrated into people's daily life, so while adapting to this mode of life, People are also looking for more comfortable experience and enjoyment of QoS as a standard of measurement. Seeking to use minimum cost to obtain the largest network resources based on QoS has become a major direction of network research. In this paper, aiming at the defects of ant colony algorithm, In this paper, the problems of long routing time, poor global convergence and easy to fall into local optimization are analyzed. The cloud model is used to optimize the original basic ant colony algorithm, and the efficiency of the algorithm is improved. In this paper, the QoS multicast routing model is used to simulate, and the related knowledge is described in the middle of the paper. This paper uses wireless routing to construct the mathematical model of multicast routing algorithm, combines ant colony algorithm and cloud model to optimize the network overhead, and finally verifies the data. The theoretical feasibility of the algorithm is obtained. Due to the positive feedback mechanism of ant population, the algorithm is easy to fall into local optimum. In this paper, the pheromone volatilization strategy is improved, the cloud model is used for dynamic adaptive programming, and the local pheromone updating strategy is dynamically adjusted. The global convergence of the algorithm is improved by adopting the latest optimal worst path updating rules in the overall pheromone updating strategy. The simulation results show that the cloud ant colony algorithm (CACA) is effective in solving routing problems. Compared with the traditional QoS multicast routing algorithm, the convergence rate and the cost tree are improved greatly.
【学位授予单位】：辽宁科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP393.09

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