Hybrid Optimize Strategy based QoS Route Algorithm for Mobile Ad hoc Networks

It is very difficult to find feasible QoS (Quality of service) routes in the mobile ad hoc networks (MANETs), because of the nature constrains of it, such as dynamic network topology, wireless communication link and limited process capability of nodes. In order to reduce average cost in flooding path discovery scheme of the traditional MANETs routing protocols and increase the probability of success in finding QoS feasible paths and we proposed a heuristic and distributed route discovery method named RLGAMAN that supports QoS requirement for MANETs in this study. This method integrates a distributed route discovery scheme with a reinforcement learning (RL) method that only utilizes the local information for the dynamic network environment; and the route expand scheme based on genetic algorithms (GA) method to find more new feasible paths and avoid the problem of local optimize. We investigate the performance of the RLGAMAN by simulation experiment bed in NS2. Compared with traditional method, the experiment results showed the network performance is improved obviously and RLGAMAN is efficient and effective. Key word: QoS route discovery, genetic algorithms, reinforcement learning, mobile ad hoc networks