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Ant colony system: a cooperative learning approach to the traveling salesman problem

Marco Dorigo; Luca Maria Gambardella · IEEE Transactions on Evolutionary Computation · 1997

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This paper introduces the ant colony system (ACS), a distributed algorithm that is applied to the traveling salesman problem (TSP). In the ACS, a set of cooperating agents called ants cooperate to find good solutions to TSPs. Ants cooperate using an indirect form of communication mediated by a pheromone they deposit on the edges of the TSP graph while building solutions. We study the ACS by running experiments to understand its operation. The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and we conclude comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs.

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APA 7

Dorigo, M. & Gambardella, L. M. (1997). Ant colony system: a cooperative learning approach to the traveling salesman problem. https://doi.org/10.1109/4235.585892

MLA

Dorigo, Marco, and Luca Maria Gambardella. "Ant colony system: a cooperative learning approach to the traveling salesman problem." 1997. https://doi.org/10.1109/4235.585892.

Chicago

Dorigo, Marco and Luca Maria Gambardella. 1997. "Ant colony system: a cooperative learning approach to the traveling salesman problem.". https://doi.org/10.1109/4235.585892.

Harvard

Dorigo, M. and Gambardella, L. M. 1997, Ant colony system: a cooperative learning approach to the traveling salesman problem, IEEE Transactions on Evolutionary Computation, available at: https://doi.org/10.1109/4235.585892 [Accessed 7 Aug. 2026].

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Title
Ant colony system: a cooperative learning approach to the traveling salesman problem
Author / contributors
Marco Dorigo; Luca Maria Gambardella
Publisher
IEEE Transactions on Evolutionary Computation
Publication year
1997
Language
English

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