Ant colony system for the multi objective problems using the collective knowledge center

this source code proposes a dynamic parameterization approach to the ant colony optimization algorithm configuration applied to multi-objective optimization problems. Indeed, the inertia of the static vision of the pheromone or visibility preferences values makes our dynamic approach a desired approach. We propose a model based on a collective knowledge center shared by the colony members, storing the best configurations based on the old experiments of the colony during the learning phase on random problems. The construction of this center is based on a statistical and qualitative study of the evaluation criteria that will be explained over the paper. Our model gives results that show a rise in quality of the outputs, as well as a proof of concept of the artificial learning approach.