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Encyclopedia of Machine Learning pp 36–39Cite as

Ant Colony Optimization

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Synonyms

ACO

Definition

Ant colony optimization (ACO) is a population-based metaheuristic for the solution of  difficult combinatorial optimization problems. In ACO, each individual of the population is an artificial agent that builds incrementally and stochastically a solution to the considered problem. Agents build solutions by moving on a graph-based representation of the problem. At each step their moves define which solution components are added to the solution under construction. A probabilistic model is associated with the graph and is used to bias the agents’ choices. The probabilistic model is updated on-line by the agents so as to increase the probability that future agents will build good solutions.

Motivation and Background

Ant colony optimization is so called because of its original inspiration: the foraging behavior of some ant species. In particular, in Beckers, Deneubourg, and Goss (1992) it was demonstrated experimentally that ants are able to find the shortest path...

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Recommended Reading

  • Beckers, R., Deneubourg, J. L., & Goss, S. (1992). Trails and U-turns in the selection of the shortest path by the ant Lasius Niger. Journal of Theoretical Biology, 159, 397–415.

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  • Dorigo, M., & Gambardella, L. M. (1997). Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1), 53–66.

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  • Dorigo, M., Maniezzo, V., & Colorni, A. (1991). Positive feedback as a search strategy. Technical Report 91-016, Dipartimento di Elettronica, Politecnico di Milano, Milan, Italy.

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  • Dorigo M., Maniezzo V., & Colorni A. (1996). Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics – Part B, 26(1), 29–41.

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  • Dorigo, M., & Stützle, T. (2004). Ant colony optimization. Cambridge, MA: MIT Press.

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  • Maniezzo, V. (1999). Exact and approximate nondeterministic tree-search procedures for the quadratic assignment problem. INFORMS Journal on Computing, 11(4), 358–369.

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  • Stützle, T., & Hoos, H. H. (1997). The \(\mathcal{M}\mathcal{A}\mathcal{X}\)–\(\mathcal{M}\mathcal{I}\mathcal{N}\) ant system and local search for the traveling salesman problem. In Proceedings of the 1997 Congress on Evolutionary Computation – CEC’97 (pp. 309–314). Piscataway, NJ: IEEE Press.

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  • Stützle, T., & Hoos, H. H. (2000). \(\mathcal{M}\mathcal{A}\mathcal{X}\)–\(\mathcal{M}\mathcal{I}\mathcal{N}\) ant system. Future Generation Computer Systems, 16(8), 889–914, 2000.

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Author information

Authors and Affiliations

    Authors
    1. Marco Dorigo
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    2. Mauro Birattari
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    Editor information

    Editors and Affiliations

    1. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia, 2052

      Claude Sammut

    2. Faculty of Information Technology, Clayton School of Information Technology, Monash University, P.O. Box 63, Victoria, Australia, 3800

      Geoffrey I. Webb

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    © 2011 Springer Science+Business Media, LLC

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    Dorigo, M., Birattari, M. (2011). Ant Colony Optimization. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_22

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