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Australasian Joint Conference on Artificial Intelligence

AI 2005: AI 2005: Advances in Artificial Intelligence pp 786–789Cite as

Understanding the Pheromone System Within Ant Colony Optimization

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3809))

Abstract

Ant Colony Optimization (ACO) is a collection of metaheuristics inspired by foraging in ant colonies, whose aim is to solve combinatorial optimization problems. We identify some principles behind the metaheuristics’ rules; and we show that ensuring their application, as a correction to a published algorithm for the vertex cover problem, leads to a statistically significant improvement in empirical results.

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References

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  4. Gilmour, S., Dras, M.: Understanding the Pheromone System within Ant Colony Optimmization. MS. Macquarie University (September 2005)

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  6. Shyu, S.J., Yin, P.-Y., Lin, B.M.T.: An Ant Colony Optimization Algorithm for the Minimum Weight Vertex Cover Problem. Annals of Operational Research 131, 283–304 (2004)

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Author information

Authors and Affiliations

  1. Department of Computing, Macquarie University, North Ryde, 2109, Australia

    Stephen Gilmour & Mark Dras

Authors
  1. Stephen Gilmour
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  2. Mark Dras
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Editor information

Editors and Affiliations

  1. Guangxi Normal University, College of CS and IT, Guilin, China, and University of Technology, Faculty of Engineering and Information Technology, Sydney, Australia

    Shichao Zhang

  2. Department of Electrical and Computer Systems Engineering, Monash University, 3800, Melbourne, Victoria, Australia

    Ray Jarvis

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© 2005 Springer-Verlag Berlin Heidelberg

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Gilmour, S., Dras, M. (2005). Understanding the Pheromone System Within Ant Colony Optimization. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_81

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  • DOI: https://doi.org/10.1007/11589990_81

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30462-3

  • Online ISBN: 978-3-540-31652-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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