A multiple pheromone ant clustering algorithm

Research output: Chapter in Book/Report/Conference proceedingConference publication

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.
Original languageEnglish
Title of host publicationNature Inspired Cooperative Strategies for Optimization (NICSO 2013)
Subtitle of host publicationlearning, optimization and interdisciplinary Aapplications
EditorsGerman Terrazas, Fernando E.B. Otero, Antonio D. Masegosa
Place of PublicationCham (CH)
PublisherSpringer
Pages13-27
Number of pages15
ISBN (Electronic)978-3-319-01692-4
ISBN (Print)978-3-319-01691-7
DOIs
Publication statusPublished - 31 Dec 2014
Event6th international workshop on Nature Inspired Cooperative Strategies for Optimization - Canterbury, United Kingdom
Duration: 2 Sep 20134 Sep 2013

Publication series

NameStudies in computational intelligence
PublisherSpringer
Volume512
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Workshop

Workshop6th international workshop on Nature Inspired Cooperative Strategies for Optimization
Abbreviated titleNICSO 2013
CountryUnited Kingdom
CityCanterbury
Period2/09/134/09/13

Keywords

  • ant colony algorithms
  • emergent behaviour
  • swarm intelligence
  • cluster analysis
  • classification

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  • Cite this

    Chircop, J., & Buckingham, C. D. (2014). A multiple pheromone ant clustering algorithm. In G. Terrazas, F. E. B. Otero, & A. D. Masegosa (Eds.), Nature Inspired Cooperative Strategies for Optimization (NICSO 2013): learning, optimization and interdisciplinary Aapplications (pp. 13-27). (Studies in computational intelligence; Vol. 512). Springer. https://doi.org/10.1007/978-3-319-01692-4_2