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. In previous work, we evaluated this variation with the M M AS Ant System model and the Traveling Salesman Problem (TSP), and found that it can both improve solution quality and reduce execution-time. In this paper, we evaluate stubborn ants with Ranked Ant System, and find that performance also improves in terms of solution quality and execution time.
A. M. Abdelbar and D. C. Wunsch, "Promoting Search Diversity in Ant Colony Optimization with Stubborn Ants," Procedia Computer Science, vol. 12, pp. 456-462, Elsevier, Jan 2012.
The definitive version is available at http://dx.doi.org/10.1016/j.procs.2012.09.104
2012 Complex Adaptive Systems Conference (2012: Nov. 14-16, Washington, DC)
Electrical and Computer Engineering
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