Improving the performance of MAX-MIN ant system on the TSP using stubborn ants

  • Authors:

  • Ashraf M. Abdelbar;Donald C. Wunsch

  • Affiliations:

  • American University in Cairo, Cairo, Egypt;Missouri University of Science and Technology, St. Louis, MO, USA

  • Venue:
  • Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
  • Year:
  • 2012

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Abstract

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 and heuristic 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, and find that it improves solution quality to a statistically-significant extent.