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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 et al. (1992) it was demonstrated experimentally that ants are able to find the shortest path between their...
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Beckers R, Deneubourg JL, Goss S (1992) Trails and U-turns in the selection of the shortest path by the ant Lasius Niger. J Theor Biol 159:397–415
Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66
Dorigo M, Stützle T (2004) Ant colony optimization. MIT Press, Cambridge
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Maniezzo V (1999) Exact and approximate nondeterministic tree-search procedures for the quadratic assignment problem. INFORMS J Comput 11(4):358–369
Stützle T, Hoos HH (1997) The \(\mathcal{M}AX -\mathcal{M}IN\) ant system and local search for the traveling salesman problem. In: Proceedings of the 1997 congress on evolutionary computation – CEC’97. IEEE Press, Piscataway, pp 309–314
Stützle T, Hoos HH (2000) \(\mathcal{M}AX -\mathcal{M}IN\) ant system. Future Gener Comput Syst 16(8):889–914
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Dorigo, M., Birattari, M. (2017). Ant Colony Optimization. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_22
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_22
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