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Ant Colony System: A cooperative learning approach to the traveling salesman problem (1997)
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| Venue: | IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION |
| Citations: | 994 - 52 self |
BibTeX
@ARTICLE{Dorigo97antcolony,
author = {Marco Dorigo and Luca Maria Gambardella},
title = {Ant Colony System: A cooperative learning approach to the traveling salesman problem},
journal = {IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION},
year = {1997}
}
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Abstract
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 TSP’s. 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 TSP’s.
