Adhoc Network Routing Optimization and Performance Analysis of ACO Based Routing Protocol

Ant Colony Optimization is based on the capability of real ant colonies of finding the shortest path from nest to food source by depositing and following the trail of pheromone on the path. Pheromone is a chemical deposited on ground by the ants while walking and it affect their moving decisions based on the intensity. The main components for ACO optimization are: set of software agent (artificial ant), use of memory and strategies of collective and distributed learning. Many ACO algorithms were designed and implemented for TSP which was very successful in finding optimal tour. In MANET routing is a challenging issue because of dynamically changing network topology which needs to be addressed. In this paper we analyze an ACO based routing algorithm inspired by the foraging behavior of ants, to route packets through shorter and feasible routes. Each ant while moving towards destination collects information about the time length, congestion status and address of each visited node of the followed path. During backward travel, local network traffic model and routing table is modified by ant based on the goodness of the followed path. The protocol is capable of discovering path for dynamic changing topology and varying traffic over the network and gives high redundancy and fault tolerance. Analysis shows that the proposed ACO based routing protocol gives performance improvement over other routing protocol by continuously checking for better paths in the network with less overhead.