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Updating ACO Pheromones Using Stochastic Gradient Ascent and Cross-Entropy Methods

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Book cover Applications of Evolutionary Computing (EvoWorkshops 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2279))

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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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References

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Author information

Authors and Affiliations

  1. IRIDIA, Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  2. Dept. of Computer Science, Technion - Israel Institute of Technology, Haifa, Israel

    Mark Zlochin

  3. IRIDIA, Université Libre de Bruxelles, Brussels, Belgium

    Nicolas Meuleau

  4. Intellektik, Darmstadt University of Technology, Darmstadt, Germany

    Mauro Birattari

Authors
  1. Marco Dorigo
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  2. Mark Zlochin
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  3. Nicolas Meuleau
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  4. Mauro Birattari
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Editor information

Editors and Affiliations

  1. Dept.of Computer Engineering, University of Parma, Parco Area delle Scienze 181/a, 43100, Parma, Italy

    Stefano Cagnoni

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© 2002 Springer-Verlag Berlin Heidelberg

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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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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43432-0

  • Online ISBN: 978-3-540-46004-6

  • eBook Packages: Springer Book Archive

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