摘要
针对现有的求解多约束QoS组播路由中存在的问题,结合BP神经网络局部搜索的优势和蚁群算法全局搜索的优势的特点,进行QoS组播路由算法的设计,提出了一种新型的NNAC算法。该算法通过BP神经网络寻找路径的更优解,改善了QoS组播路由路径寻找的方法。通过实验仿真表明,NNAC算法得到最优组播树的总延时为35,总代价费用为21,在完成150个度约束组播路由路径时,NNAC算法在进行最优组播树的寻找成功率上高于AC算法,同时该算法还克服了AC算法易陷入局部最小点的不足。
Aiming at solving QoS muhicast routing problem, the advantages of BP neural network at local search and ant colony algorithm at global search are combined,and the QoS multicast routing algorithm is designed. In this paper, a new NNAC algorithm is presented. The algorithm using BP neural network to find a better path, improves QoS muhicast routing path finding method. Simulation results indicate that, the total delay of the NNAC algorithm to get the optimal multicast tree is 35, the total cost is 21. On the completion of the 150 degree constrained multicast routing path, the NNAC algorithm in the optimal multicast tree search success rate is higher than that of AC algorithm. While the algorithm overcomes the AC algorithm is easy to fall into local minimum point defects.
出处
《电视技术》
北大核心
2014年第7期112-114,181,共4页
Video Engineering
基金
河南省科技厅基础与前沿项目(132300410204)
河南省科技攻关计划项目(122102210407)
河南省哲学社会科学规划项目(2012CJJ014)
关键词
组播路由
神经网络
蚁群算法
信息调整
仿真分析
multicast routing
neural network
ant colony algorithm
information adjust
simulation analysis
