Ad Hoc网络Q学习稳定蚁群路由算法

摘要

针对Ad Hoc网络路由协议存在的对动态拓扑适应性差和链路不稳定问题,提出了一种Q学习稳定蚁群路由算法（SACRQ）,该算法综合了蚁群优化和Q学习算法的思想,将信息素映射为Q学习算法的Q值,增强节点对动态环境的学习能力.在路由选择方面,使用自适应伪随机比率选择下一跳节点,避免算法陷入局部最优或是停滞;提出了新的链路稳定度来衡量链路的鲁棒性,结合鲁棒性和信息素强度两种因素选择下一跳链路.该算法增加了链路的鲁棒性,对Ad Hoc网络动态拓扑适应性强.仿真结果表明,SACRQ的路由发现数量、平均端对端延迟、冲突数量和每次路由发现吞吐量4种指标均优于ARA和AODV.%To solve the problem of poor flexibility and frequent route breakage caused by dynamic topology in Ad Hoc network routing protocols,a stable ant colony routing algorithm based on Q-learning（SACRQ） is proposed,which synthesizes the Ant Colony Optimization and the Q-learning algorithm.The pheromone level is equal to the Q value to enhance the learning ability of nodes.To avoid local peak,SARCQ applies an adaptive pseudo random proportional action choice rule to select the next hop.A new robustness of the links metric is presented to calculate the probability of the route selection together with the pheromone level.The algorithm enhances the stability of the links and demonstrates high flexibility to the dynamic topology of the network.Simulation results show that SACRQ achieves better performance in terms of the number of the route discovery,the average end-to-end delay,the number of collisions and the average throughput per route discovery,which is respectively compared with the ARA and AODV.