Two-Dimensional Pheromone in Ant Colony Optimization

Ant Colony Optimization (ACO) is an acclaimed method for solving combinatorial problems proposed by Marco Dorigo in 1992 and has since been enhanced and hybridized many times. This paper proposes a novel modification of the algorithm, based on the introduction of a two-dimensional pheromone into a single-criteria ACO. The complex structure of the pheromone is supposed to increase ants’ awareness when choosing the next edge of the graph, helping them achieve better results than in the original algorithm. The proposed modification is general and thus can be applied to any ACO-type algorithm. We show the results based on a representative instance of TSPLIB and discuss them in order to support our claims regarding the efficiency and efficacy of the proposed approach.