Ant Colony Optimization and Its Application

In this paper, a methods based on the Ant Colony Optimization(ACO) paradigm are proposed: the Multiple Objective Network optimization based on an ACO (MONACO). The MONACO mimics the ant's foraging behaviour supported by a communication process, which relies on a chemical pheromone trail, signalling a good path to some supply location. One of the main features, which differentiates MONACO from almost all the others Ant Colony algorithms, is the fact that MONACO process uses a pheromone vector associated to each atomic piece, as if there were several layers of pheromones. Each component of those vectors is associated to a weight and, as before, represents how worthy the elements were, for some time-window, in the construction of the solutions relatively to the associated weight.