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A Multiple Pheromone Ant Clustering Algorithm

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Nature Inspired Cooperative Strategies for Optimization (NICSO 2013)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 512))

Abstract

Ant colony optimisation algorithms model the way ants use pheromones for marking paths to important locations in their environment. Pheromone traces are picked up, followed, and reinforced by other ants but also evaporate over time. Optimal paths attract more pheromone and less useful paths fade away. The main innovation of the proposed Multiple Pheromone Ant Clustering Algorithm (MPACA) is to mark objects using many pheromones, one for each value of each attribute describing the objects in multidimensional space. Every object has one or more ants assigned to each attribute value and the ants then try to find other objects with matching values, depositing pheromone traces that link them. Encounters between ants are used to determine when ants should combine their features to look for conjunctions and whether they should belong to the same colony. This paper explains the algorithm and explores its potential effectiveness for cluster analysis.

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Authors and Affiliations

  1. Aston University, Birmingham, United Kingdom

    Jan Chircop & Christopher D. Buckingham

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  1. Jan Chircop
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  2. Christopher D. Buckingham
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Correspondence to Jan Chircop .

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Editors and Affiliations

  1. School of Computer Science, University of Nottingham, Nottingham, United Kingdom

    German Terrazas

  2. School of Computing, University of Kent, Canterbury, Kent, United Kingdom

    Fernando E. B. Otero

  3. Center for Research on ICT, University of Granada, Granada, Spain

    Antonio D. Masegosa

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Chircop, J., Buckingham, C.D. (2014). A Multiple Pheromone Ant Clustering Algorithm. In: Terrazas, G., Otero, F., Masegosa, A. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2013). Studies in Computational Intelligence, vol 512. Springer, Cham. https://doi.org/10.1007/978-3-319-01692-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-01692-4_2

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-01691-7

  • Online ISBN: 978-3-319-01692-4

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