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Running Time Analysis of ACO Systems for Shortest Path Problems

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Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics (SLS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5752))

Abstract

Ant Colony Optimization (ACO) is inspired by the ability of ant colonies to find shortest paths between their nest and a food source. We analyze the running time of different ACO systems for shortest path problems. First, we improve running time bounds by Attiratanasunthron and Fakcharoenphol [Information Processing Letters, 105(3):88–92, 2008] for single-destination shortest paths and extend their results for acyclic graphs to arbitrary graphs. Our upper bound is asymptotically tight for large evaporation factors, holds with high probability, and transfers to the all-pairs shortest paths problem. There, a simple mechanism for exchanging information between ants with different destinations yields a significant improvement. Our results indicate that ACO is the best known metaheuristic for the all-pairs shortest paths problem.

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Author information

Authors and Affiliations

  1. LS 2, Fakultät für Informatik, Technische Universität Dortmund, Dortmund, Germany

    Christian Horoba & Dirk Sudholt

  2. International Computer Science Institute, Berkeley, USA

    Dirk Sudholt

Authors
  1. Christian Horoba
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  2. Dirk Sudholt
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Editor information

Editors and Affiliations

  1. IRIDIA, CoDE, Université Libre de Bruxelles, Avenue F. Roosevelt 50, CP 194/6, 1050, Brussels, Belgium

    Thomas Stützle

  2. IRIDIA, CoDE, Université Libre de Bruxelles, Av. F. Roosevelt 50, CP 194/6, 1050, Brussels, Belgium

    Mauro Birattari

  3. Computer Science Department, University of British Columbia, 2366 Main Mall, V6T 1Z4, Vancouver, BC, Canada

    Holger H. Hoos

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© 2009 Springer-Verlag Berlin Heidelberg

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Horoba, C., Sudholt, D. (2009). Running Time Analysis of ACO Systems for Shortest Path Problems. In: Stützle, T., Birattari, M., Hoos, H.H. (eds) Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics. SLS 2009. Lecture Notes in Computer Science, vol 5752. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03751-1_6

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  • DOI: https://doi.org/10.1007/978-3-642-03751-1_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03750-4

  • Online ISBN: 978-3-642-03751-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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