Improving the Performance of MAX-MIN Ant System on the TSP Using Stubborn Ants

In ant colony optimization (ACO) methods, including Ant System and MAX-MIN Ant System, each ant stochastically generates its candidate solution, in a given iteration, based on the same pheromone τ and heuristic η information as every other ant. Stubborn ants is an ACO variation in which if an ant generates a particular candidate solution in a given iteration, then the components of that solution will have a higher probability of being selected in the candidate solution generated by that ant in the next iteration. We evaluate this variation in the context of MAX-MIN Ant System using 41 instances of the Traveling Salesman Problem (TSP), and find that it improves solution quality to a statistically-significant extent