Application of GA and Multi-objective Optimization for QoS Routing in Ad-Hoc Networks

Much work has been done on routing in Ad-hoc networks, but the proposed routing solutions only deal with the best effort data traffic. Connections with Quality of Service (QoS) requirements, such as voice channels with delay and bandwidth constraints, are not supported. The QoS routing has been receiving increasingly intensive attention, but searching for the shortest path with many metrics is an NP-complete problem. For this reason, approximated solutions and heuristic algorithms should be developed for multi-path constraints QoS routing. Also, the routing methods should be adaptive, flexible, and intelligent. In this paper, we use Genetic Algorithms (GAs) and Multi-objective Optimization for QoS routing in Ad-hoc Networks. In order to reduce the search space of GA, we implemented a Search Space Reduction Algorithm (SSRA). After the reduction of search space the GAMAN search time improves. Our proposed method has the best performance for crossover rate 70% and mutation rate 8%.