Abstract
In this paper we introduce two systematic approaches, based on the stochastic gradient ascent algorithm and the cross-entropy method, for deriving the pheromone update rules in the Ant colony optimization metaheuristic. We discuss the relationships between the two methods as well as connections to the update rules previously proposed in the literature.
This work was carried out while the author was at IRIDIA, Université Libre de Bruxelles, Belgium.
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Dorigo, M., Zlochin, M., Meuleau, N., Birattari, M. (2002). Updating ACO Pheromones Using Stochastic Gradient Ascent and Cross-Entropy Methods. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds) Applications of Evolutionary Computing. EvoWorkshops 2002. Lecture Notes in Computer Science, vol 2279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46004-7_3
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DOI: https://doi.org/10.1007/3-540-46004-7_3
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