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Evolution in Swarm Intelligence: An Evolutionary Ant-Based Optimization Algorithm

  • Conference paper
Ant Colony Optimization and Swarm Intelligence (ANTS 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4150))

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Abstract

Swarm Intelligent (SI) algorithms draw their inspiration from the interaction of individuals of social organisms. One such algorithm, Ant Colony Optimization (ACO) [1], utilizes the foraging behavior of ants to solve combinatorial optimization problems. Although ACO performs well in a static environment, it has been pointed out that ACO does not perform as well as other heuristics in dynamic situations such as routing. This paper proposes a new algorithm, entitled Evolutionary Ant Colony Optimization (EACO), that combines ACO with elements of Genetic Algorithms (GA). By adding evolution, the EACO algorithm allows the individual ants to develop their own characteristics, thereby removing the homogeneity inherent within ACO. Our results demonstrate the potential of this approach in a dynamic environment.

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References

  1. Dorigo, M., Di Caro, G.: The ant colony optimization meta-heuristic. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 11–32. McGraw-Hill, London (1999)

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  2. White, T., Pagurek, B., Oppacher, F.: ASGA: Improving the ant system by integration with genetic algorithms. In: Koza, J.R., Banzhaf, W., Chellapilla, K., Deb, K., Dorigo, M., Fogel, D.B., Garzon, M.H., Goldberg, D.E., Iba, H., Riolo, R. (eds.) Genetic Programming 1998: Proceedings of the Third Annual Conference, University of Wisconsin, Madison, Wisconsin, USA, pp. 610–617. Morgan Kaufmann, San Francisco (1998)

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Authors and Affiliations

  1. Department of Computer Sciences, Florida Institute of Technology, Melbourne, FL, USA

    Christopher Roach & Ronaldo Menezes

Authors
  1. Christopher Roach
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  2. Ronaldo Menezes
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Editors and Affiliations

  1. IRIDIA, CoDE, Université Libre de Bruxelles,,, Brussels, Belgium

    Marco Dorigo

  2. Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), Lugano, Switzerland

    Luca Maria Gambardella

  3. IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium

    Mauro Birattari

  4. Swarm-Intelligent Systems Group, École Polytechnique Fédérale de Lausanne, 1015, Lausanne, Switzerland

    Alcherio Martinoli

  5. Department of Computing and Electronic Systems, University of Essex, Wivenhoe Park, CO4 3SQ, Colchester, UK

    Riccardo Poli

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

    Thomas Stützle

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© 2006 Springer-Verlag Berlin Heidelberg

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Roach, C., Menezes, R. (2006). Evolution in Swarm Intelligence: An Evolutionary Ant-Based Optimization Algorithm. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2006. Lecture Notes in Computer Science, vol 4150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11839088_57

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  • DOI: https://doi.org/10.1007/11839088_57

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-38482-3

  • Online ISBN: 978-3-540-38483-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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