改进Pareto蚁群算法的多QoS组播路由策略  

An Improved Pareto Ant Colony Optimization for Multicast Routing Scheme with Multi-QoS Constraints

作　　者：蒲汛[1,2] 于显平[2] 卢显良[1] 

机构地区：[1]电子科技大学计算机学院,成都410073 [2]西南大学计算机与信息科学学院,重庆400716

出　　处：《小型微型计算机系统》2012年第12期2575-2579,共5页Journal of Chinese Computer Systems

基　　金：国家科技重大专项项目(2010ZX03004-001-02;2011ZX03002-003-02)资助

摘　　要：满足多约束条件的QoS(Quality of Service)组播路由是新一代网络信息传输的一项关键技术之一,提出一种改进Pare-to蚁群算法(MPACO)对该问题进行求解.通过分析QoS组播路由问题模型,针对QoS参数以及网络信息不确定的情况,使用分类蚁群快速查找出在给定QoS条件下满足需求概率最大的组播树Pareto非劣解.在算法执行过程中引入局部信息节点惩罚更新机制,以降低无关节点的重复搜索率;同时在全局信息素更新阶段,引入了新的激励更新机制,以期提高算法的收敛速度.仿真实验表明,该机制是可行和有效的,并能较快搜索到全局(近似)最优解.Multi-constrained Quality of Service （ QoS ） multicast routing is a key technology of next generation Internet. A modify Pa- reto Ant Colony Optimization { MPACO }, based on Pareto Ant Colony and fuzzy mathematics, was proposed to solve the problem in this paper. After analyzed the mathematical description of inaccurate information network, we used multi-group of ant agents to find the non dominated Pareto set with the maximum probability of meeting with multiple QoS constraints, then select the best multicast tree from that set. And at runtime, MPACO update the pheromone values using a new strategy which was improved to be more suit- able for inaccurate information network. Simulation results have shown that the proposed algorithm is both feasible and effective.

关 键 词：蚁群算法 PARETO最优 QOS约束 组播树 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]