Meta-heurística Híbrida de Sistema de Colônia de Formigas e Algoritmo Genético para o Problema do Caixeiro Viajante
DOI:
https://doi.org/10.5540/tema.2008.09.01.0031Abstract
Apresentamos neste artigo, uma nova Meta-heurística Híbrida de Sistema de Colônia de Formigas (ACS) e Algoritmos Genéticos (AG) para resolver o Problema do Caixeiro Viajante (PCV). A resolução do Problema do Caixeiro Viajante é complexa, pois envolve uma busca em um enorme espaço de soluções que cresce conforme aumenta o número de nós do grafo, tornando inviável a utilização de métodos exatos. O Algoritmo Híbrido ACS+AG-PCV é proposto visando obter bons resultados, de maneira a contornar a questão da complexidade do Problema do Caixeiro Viajante.References
[1] M. Affenzeller, S.Wagner, A self-adaptive model for selective pressure handling within the of genetic algorithms. In: “Computer Aided Systems Theory”, Eurocast, Spriger Verlag, 2809, pp. 384-393, 2003.
E. Bonabeau, M. Dorigo, G. Theraulaz, “Swarm Intelligence: From Natural to Artificial Systems”, Oxford University Press, New York, 1999.
Bullnheimer, R.F. Hartz, C. Strauss, An improved ant system algorithm for the vehicle routing problem, Annals of Operations Research, 1999.
M. Dorigo, “Ottimizzazione, apprendimento automatico, ed algoritmi basati sumetafora naturale (Optimization, Learning, and Natural Algorithms)”, Ph.D.
Dissertation, Politecnico di Milano, Press, 1992.
M. Dorigo, L. M. Gambardella, Ant colonies for the traveling salesman problem, BioSystems, 43 (1997), 73-81.
M. Dorigo, T. St¨utzle, ACO Algorithms for the Traveling Salesman Problem, In Evolutionary Algorithms in Engineering and Computer Science (K. Miettinen, M. Makela, P. Neittaanmaki, J. Periaux, eds.) Wiley, 1999.
M.R. Garey, D.S. Johnson, “Computers and Intractability: a Guide to the Theory of NP-Completeness”, San Francisco, CA: W. H. Freeman, 1979.
J.H. Holland, “Adaptation in Natural and Artificial Systems”, 2 ed., MIT Press, MA, USA, 1992.
S. Lin, B.W. Kernighan, An effective heuristic algorithm for the traveling salesman problem. Operations Research, 21 (1973), 498-516.
P. Merz, B. Freisleber, Genetic Local Search for the TSP: New results. In: IEEE International Conference on Evolutionary, IEEE Press, pp. 159-164, Indaianapolis, 1997.
Z. Michalewicz, “Genetic Algorithms + Data Structures = Evolution Programs”, Springer-Verlag, Berlin, 1996.
M.L. Pilat, T. White, Using genetic algorithms to optimize ACS-TSP, in “Proceedings of the Third International Workshop on Ant Algorithms - ANTS”, pp. 282-287, Springer-Verlag, 2002.
G. Reinelt, TSPLIB: A travelling salesman problem library. ORSA Journal on Computing, 3 (1991), 376-384.
S.C. Sari, H.D. Sherali, A. Bhootra, New tighter polynomial length formulations for the asymmetric traveling salesman problem with and without precedence
constraints. Operations Research Letters, 33, No. 1 (2007), 62-70.
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