MANET link performance using Ant Colony Optimization and Particle Swarm Optimization algorithms

End-to-end delay and Communication cost are the most important metrics in MANET (Mobile Adhoc Network) routing from source to destination. Recent approaches in Swarm intelligence (SI) technique, a local interaction of many simple agents to meet a global goal, prove that it has more impact on routing in MANETs. Ant Colony Optimization (ACO) algorithm uses mobile agents as ants to discover feasible and best path in a network. ACO helps in finding the paths between two nodes in a network and acts as an input to the Particle Swarm Optimization (PSO) technique, a metaheuristic approach in SI. PSO finds the best solution over the particle´s position and velocity with the objective of cost and minimum End-to-end delay. This hybrid algorithm exhibits better performances when compared to ACO approach.