I present this site, a stochastic meta-heuristic inspired by
nature, based on Ant Colony Optimization for (ACO), originally formulated for
combinatorial optimization problems, and a Genetic Algorithm (AntNet) to solve
the problem of routing in IP networks. Solving the problem of routing in IP
networks is complex because it involves a search in a huge search space that
grows as the number of nodes, making it impractical to use exact methods. The proposed
algorithm is Antnet to obtain good results, so as to circumvent the question of
the complexity of the problem.
The ant colony optimization (ACO) is a new meta-heuristic that mimics the behavior of a population of agents (ants) in search of food. Through the use of cooperation mechanisms and adaptation, this technique emulates nature so as to obtain promising solutions with simple ideas.
With this, the ACO is proving to be a competitive approach in relation to other strategies presented in the literature. Regarding the application of this technique, the same can be applied in a wide range of problems, being among the best known, for example, the problem of vehicle routing, restoration of electrical power systems, routing in IP networks.
The ant colony optimization (ACO) is a new meta-heuristic that mimics the behavior of a population of agents (ants) in search of food. Through the use of cooperation mechanisms and adaptation, this technique emulates nature so as to obtain promising solutions with simple ideas.
With this, the ACO is proving to be a competitive approach in relation to other strategies presented in the literature. Regarding the application of this technique, the same can be applied in a wide range of problems, being among the best known, for example, the problem of vehicle routing, restoration of electrical power systems, routing in IP networks.
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