Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Ant Colony Optimization

  • Marco Dorigo
  • Mauro Birattari
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_22




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

  1. 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.Google Scholar
  2. 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.Google Scholar
  3. 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.Google Scholar
  4. 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.Google Scholar
  5. Dorigo, M., & Stützle, T. (2004). Ant colony optimization. Cambridge, MA: MIT Press.zbMATHGoogle Scholar
  6. Maniezzo, V. (1999). Exact and approximate nondeterministic tree-search procedures for the quadratic assignment problem. INFORMS Journal on Computing, 11(4), 358–369.zbMATHMathSciNetGoogle Scholar
  7. 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.Google Scholar
  8. 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.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Marco Dorigo
  • Mauro Birattari

There are no affiliations available