Tree-Based Ant Colony Optimization Algorithm for Effective Multicast Routing in Mobile Adhoc Network

Background: Multimedia transmission over wireless communication is gaining momentum with rapid use of mobile hand-held devices. Providing a QoS based routing solution is a major challenge, due to the transient and inaccurate state of Mobile Ad hoc Networks. Discovering optimal multicast routes is an NP-Problem and hence, QoS based routing is typically an optimization problem. Swarm Intelligence is a heuristic-based approach to find solutions to various complex problems using the principle of collective behaviour of natural agents. Objective: An ACO based approach for optimization of QoS based multicast routing algorithm for multimedia streaming applications is proposed. Proposed approach performed well in comparison to other state-of-the-art approaches with respect to path maintenance, packet delivery ratio, and end-toend delay. Methods: The multicast routing model is simulated as a tree structure, where the nodes represent stations and the edges represent the link between the stations. Results: Results show that proposed approach is much faster in convergence speed than the conventional AntNet. With the increasing size of the MANET environment, the convergence time of proposed approach is much better than AntNet. This is mainly due to the trace maintenance, treebased approach for path selection and implementation of local update and global update of the pheromone values. Conclusion: We can conclude that the proposed approach is a more effective algorithm for multiconstraints multicast routing.