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Ant colony optimization (ACO) takes inspiration from the foraging behaviour of some ant species. These ants deposit pheromone on the
ground in order to mark some favourable path that should be followed by other members of the colony. Ant colony optimization exploits a similar
mechanism for solving optimization problems. This protocol is highly adaptive, efficient, scalable and reduces the overhead for routing. Ant
Algorithms are used to find the shortest route in Mobile Ad Hoc Networks. ACO has been widely applied to solving various combinatorial
optimization problems such as Traveling Salesman Problem (TSP), Job-shop Scheduling Problem (JSP), Vehicle Routing Problem (VRP), Quadratic
Assignment Problem (QAP), etc. The behaviour of ACO algorithms and the ACO model are analysed for certain types of permutation problems. It is
shown analytically that the decisions of an ant are influenced in an intriguing way by the use of the pheromone information and the properties of the
pheromone matrix. This paper provides a brief outline of some significant applications of ACO algorithms. In this paper we are considering the
applications to problems with nonstandard features and we have also discussed the use of ACO in TSP. ACO is taken as one of the high performance
computing methods for TSP. Metaheuristic algorithm is an efficient method to obtain near-optimal solutions of NP-hard problems.
Keywords: metaheuristic; stochastic; swarm; overhead and mobile ad hoc network.
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