A dynamic configuration with a shared knowledge centre for multi-objective ant colony optimisation algorithms

Abstract

This paper proposes a dynamic configuration approach to the ant colony optimisation algorithm configuration, applied to multi-objective optimisation problems. Indeed, the inertia of the static vision of the pheromone or visibility preferences values makes our dynamic approach desired. We propose a model based on a collective knowledge centre shared by the colony members, storing the best configurations based on the old colony's experiments during the learning phase for random problems. The construction of this centre is based on statistical and qualitative studies 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 for the artificial learning approach.