Thursday, August 2, 2012

Introduction ACO AntNet

Currently, researchers around the world propose new methods to solve classical problems or complex, so simple and / or efficient.
A simple proof of this are the new optimization techniques based on swarm intelligence, where through cooperation between individuals, directly or indirectly, can be a better adaptation of the environment.
An example of a swarm intelligence technique is the ant colony optimization (ACO), inspired by the behavior of agents (artificial ants) in search of alimento.A ant colony optimization, originally described by Dorigo (1992), has idea primarily as indirect communication among its subjects, through the path made by each ant during the exploration of the search space. This track is made using a kind of artificial pheromone, which acts as an attraction for them, serving as information perceived by the ants which is modified to reflect your current search experience.

The motivation of this work is to implement and evaluate the feasibility of a new target for heuristic called Ant Colony Optimization (ACO - Ant Colony Optimization), in solving routing problem in IP networks. Inspired by the behavior of some species of ants the analogy of the problem is that the ants would seek the best path with lower cost and time (effort) for moths acceptable computational routes.

The whole context was simulated by me using the NS-2 algorithm with AntNet integrated three topologies were adopted, 3x4 mesh networks, ring topology using three nodes and an arbitrary topology using 12 nodes

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