Abstract
Finding the optimal solution to NP-hard problems requires at least exponential time. Thus, heuristic methods are usually applied to obtain acceptable solutions to this kind of problems. In this paper we propose a new type of heuristic algorithms to solve this kind of complex problems. Our algorithm is based on river formation dynamics and provides some advantages over other heuristic methods, like ant colony optimization methods. We present our basic scheme and we illustrate its usefulness applying it to a concrete example: The Traveling Salesman Problem.
Research partially supported by the MCYT project TIN2006-15578-C02-01, the Junta de Castilla-La Mancha project PAC06-0008-6995, and the Marie Curie project MRTN-CT-2003-505121/TAROT.
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References
Applegate, D.L., Bixby, R.E., Chvatal, V., Cook, W.J.: The Traveling Salesman Problem: A Computational Study. Princeton University Press, Princeton, NJ (2006)
Davis, L. (ed.): Handbook of genetic algorithms. Van Nostrand Reinhold, New York (1991)
Dorigo, M.: Ant Colony Optimization. MIT Press, Cambridge (2004)
Dorigo, M., Gambardella, L.M.: Ant colonies for the traveling salesman problem. BioSystems 43(2), 73–81 (1997)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man and Cybernetics, Part B 26(1), 29–41 (1996)
Fleischer, M.: Simulated annealing: past, present, and future. In: Proceedings of the 27th conference on Winter simulation, pp. 155–161 (1995)
Gutin, G., Punnen, A.P.: The Traveling Salesman Problem and Its Variations. Kluwer Academic Publishers, Dordrecht (2002)
Haupt, R.L., Haupt, S.E.: Practical Genetic Algorithms. Wiley-Interscience, New York, NY, USA (2004)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, 1995, vol. 4 (1995)
Kirkpatrick, S., Gelatt, Jr., C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220(4598), 671 (1983)
Langton, C.: Studying artificial life with cellular automata. Physica D 22, 120–149 (1986)
Reinelt, G.: TSPLIB 95. Technical report, Research Report, Institut für Angewandte Mathematik, Universität Heidelberg, Heidelberg, Germany (1995), http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/
Rosenberg, G., Salomaa, A. (eds.): Lindenmayer Systems: Impacts on Theoretical Computer Science, Computer Graphics, and Developmental Biology. Springer, Heidelberg (1992)
Wolfram, S.: Cellular Automata and Complexity. Addison-Wesley, London, UK (1994)
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Rabanal, P., Rodríguez, I., Rubio, F. (2007). Using River Formation Dynamics to Design Heuristic Algorithms. In: Akl, S.G., Calude, C.S., Dinneen, M.J., Rozenberg, G., Wareham, H.T. (eds) Unconventional Computation. UC 2007. Lecture Notes in Computer Science, vol 4618. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73554-0_16
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DOI: https://doi.org/10.1007/978-3-540-73554-0_16
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