A Multiple Pheromone Ant Clustering Algorithm

  • Jan Chircop
  • Christopher D. Buckingham
Part of the Studies in Computational Intelligence book series (SCI, volume 512)


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.


Ant Colony Algorithms Swarm Intelligence Emergent Behaviour Cluster Analysis Classification 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Aston UniversityBirminghamUnited Kingdom

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