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Improved Lower Limits for Pheromone Trails in Ant Colony Optimization

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Parallel Problem Solving from Nature – PPSN X (PPSN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5199))

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Abstract

Ant Colony Optimization algorithms were inspired by the foraging behavior of ants that accumulate pheromone trails on the shortest paths to food. Some ACO algorithms employ pheromone trail limits to improve exploration and avoid stagnation by ensuring a non-zero probability of selection for all trails. The MAX-MIN Ant System (MMAS) sets explicit pheromone trail limits while the Ant Colony System (ACS) has implicit pheromone trail limits. Stagnation still occurs in both algorithms with the recommended pheromone trail limits as the relative importance of the pheromone trails increases (α > 1). Improved estimates of the lower pheromone trail limit (τ min ) for both algorithms help avoid stagnation and improve performance for α > 1. The improved estimates suggest a general rule to avoid stagnation for stochastic algorithms with explicit or implicit limits on exponential values used in proportional selection.

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Author information

Authors and Affiliations

  1. Computer Science Department, Colorado State University, Fort Collins, Colorado, USA, 80523

    David C. Matthews

Authors
  1. David C. Matthews
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Editor information

Editors and Affiliations

  1. Fakultät für Informatik, Technische Universität Dortmund, 44221, Dortmund, Germany

    Günter Rudolph

  2. Fakultät für Informatik, Technische Universität Dortmund, 44221, Dortmund, Germany

    Thomas Jansen  & Nicola Beume  & 

  3. Department of Computing and Electronic Systems, University of Essex, CO4 3SQ, Colchester, Essex, UK

    Simon Lucas

  4. Dipartimento di Ingegneria Meccanica, Università degli Studi di Trieste, 34127, Trieste, Italy

    Carlo Poloni

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© 2008 Springer-Verlag Berlin Heidelberg

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Matthews, D.C. (2008). Improved Lower Limits for Pheromone Trails in Ant Colony Optimization. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds) Parallel Problem Solving from Nature – PPSN X. PPSN 2008. Lecture Notes in Computer Science, vol 5199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87700-4_51

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  • DOI: https://doi.org/10.1007/978-3-540-87700-4_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87699-1

  • Online ISBN: 978-3-540-87700-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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