Abstract: Cognitive network can improve the end-to-end performance of the network, and ensure QoS(Quality of Service) requirements. The existing routing algorithm does not have cognitive ability. To solve this problem, a multi-path routing algorithm of cognitiveload balancing was proposed, which combined the advantages of Q-learning algorithm and ant algorithm, to establish and maintain the route through ant algorithm, and to achieve congestion avoidance and load balancing by Q-learning algorithm. The simulation contrast with OPNET shows that the algorithm is valid and effective at controlling packet loss ratio, delay and bandwidth utilization.

Key words: multiple-path routing, cognitive networks, Q-learning algorithm, ant colony algorithm, congestion avoidance

摘要： 认知网络能够提高网络端到端的性能，确保服务质量(QoS)要求。而目前普遍使用的路由算法不具备网络认知能力。针对这一问题，提出一种具有认知能力的负载均衡多路径路由算法，该算法结合了Q学习算法和蚁群算法各自的优点，通过蚁群算法完成路径的建立和维护，Q学习算法实现拥塞规避和负载均衡。使用OPNET仿真比较，表明该算法在时延、带宽利用方面均具有较好的性能。

关键词: 多路径路由, 认知网络, Q学习算法, 蚁群算法, 拥塞避免