Abstract
In this paper we propose an ant colony optimization variant where several independent colonies try to simultaneously solve the same problem. The approach includes a migration mechanism that ensures the exchange of information between colonies and a mutation operator that aims to adjust the parameter settings during the optimization.
The proposed method was applied to several benchmark instances of the node placement problem. The results obtained shown that the multi-colony approach is more effective than the single-colony. A detailed analysis of the algorithm behavior also reveals that it is able to delay the premature convergence.
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References
Angus, D., Woodward, C.: Multiple objective ant colony optimisation. Swarm Intelligence (3), 69–85 (2009)
Bentley, J.L.: Fast algorithms for geometric traveling salesman problems. ORSA Journal on Computing 4, 387–411 (1992)
Deneubourg, J.L., Aron, S., Goss1, S., Pasteels, J.M.: The self-organizing exploratory pattern of the argentine ant. Journal of Insect Behavior 3(2) (1990)
Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization - artificial ants as a computational intelligence technique. Technical report, Université Libre de Bruxelles, Institut de Recherches Interdisciplinaires et de Développements en Intelligence Artificielle (September 2006)
Dorigo, M., Gambardella, L.M.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)
Dorigo, M., Maniezzo, V., Colorni, A.: Positive feedback as a search strategy. Tech. rep., Politecnico di Milano, Italy (1991)
Dorigo, M., Maniezzo, V., Colorni, A.: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics 26(1), 29–41 (1996)
Dorigo, M., Stutzle, T.: Ant Colony Optimization. A Bradford Book. MIT Press, Cambridge (2004)
Ellabib, I., Calamai, P., Basir, O.: Exchange strategies for multiple ant colony system. Information Sciences: an International Journal 177(5), 1248–1264 (2007)
García-Martínez, C., Cordón, O., Herrera, F.: A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria tsp. European Journal of Operational Research 180(1), 116–148 (2007)
Goss, S., Aron, S., Deneubourg, J.L., Pasteels, J.M.: Self-organized shortcuts in the argentine ant. Naturwissenschaften 76, 579–581 (1989)
Janson, S., Merkle, D., Middendorf, M.: Parallel Ant Colony Algorithms. In: Parallel Metaheuristics, pp. 171–201. John Wiley & Sons, Chichester (2005)
Katayama, K., Yamashita, H., Narihisa, H.: Variable depth search and iterated local search for the node placement problem in multihop wdm lightwave networks. In: IEEE Congress on Evolutionary Computation, pp. 3508–3515 (2007)
Kato, M., Oie, Y.: Reconfiguration algortihms based on meta-heuristics for multihop wdm lightwave networks. In: Procedings IEEE International Conference on Communications, pp. 1638–1644 (2000)
Komolafe, O., Harle, D.: Optimal node placement in an optical packet switching manhattan street network. Computer Networks (42), 251–260 (2003)
Maxemchuk, N.F.: Regular mesh topologies in local and metropolitan area networks. AT&T Technical Journal 64, 1659–1685 (1985)
Michel, R., Middendorf, M.: An ACO Algorithm for the Shortest Common Supersequence Problem. In: New ideas in optimization, pp. 51–61. McGraw-Hill, London (1999)
Middendorf, M., Reischle, F., Schmeck, H.: Multi colony ant algorithms. Journal of Heuristics 8(3), 305–320 (2002)
Stützle, T., Hoos, H.H.: The max-min ant system and local search for the traveling salesman problem. In: Piscataway, T., Bäck, Z.M., Yao, X. (eds.) IEEE International Conference on Evolutionary Computation, pp. 309–314. IEEE Press, Los Alamitos (1997)
Toyama, F., Shoji, K., Miyamichi, J.: An iterated greedy algorithm for the node placement problem in bidirectional manhattan street networks. In: Proceedings of the 10th annual conference on Genetic and evolutionary computation, pp. 579–584. ACM, New York (2008)
Tsai, C.F., Tsai, C.W., Tseng, C.C.: A new hybrid heuristic approach for solving large traveling salesman problem. Information Sciences 166(166), 67–81 (2004)
Yonezu, M., Funabiki, N., Kitani, T., Yokohira, T., Nakanishi, T., Higashino, T.: Proposal of a hierarchical heuristic algorithm for node assignment in bidirectional manhattan street networks. Systems and Computers in Japan 38(4) (2007)
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Melo, L., Pereira, F., Costa, E. (2010). MC-ANT: A Multi-Colony Ant Algorithm. In: Collet, P., Monmarché, N., Legrand, P., Schoenauer, M., Lutton, E. (eds) Artifical Evolution. EA 2009. Lecture Notes in Computer Science, vol 5975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14156-0_3
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DOI: https://doi.org/10.1007/978-3-642-14156-0_3
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