Solución del problema del agente viajero mediante clústeres y algoritmos genéticos
Keywords:
Clusters, centroids, genetic algorithm, heuristic, nodes.
Abstract
This paper tries to solve the well know Traveling Salesman Problem using clustering and genetic algorithms, so we divide a set of cities in clusters for minimize the number of cities when the genetic algorithm will be applied. Therefore we propose a new method to do the clusters, so we define K points and call it centroids. This points being clusters and we recalculate centroids so that the distance between cities and its centroids will be minimum until centroids do not change more. Then we applied the genetic algorithms on each cluster to minimize the tour´s length in each cluster. Finally we propose a method to unite all clusters minimizing the tour´s length final.
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References
Applegate, D. (2006) The traveling salesman problem: Computational study. Princeton University Press, USA.
Buthainah, F. (2008) Enhanced traveling salesman problem solving by genetic algorithm technique. World Academy of Science, Engineering and Technology, 38, 296-300.
Dantzig, G., Fulkerson R., Johnson, S. (1954) Solution of a large scale traveling salesman problem Journal of the Operations Research Society of America, 2(4), 393-410.
Chatterjee, S., Carrera, C., Lynch, L.A. (1996) Genetic algorithms and traveling salesman problem. European Journal of Operational Research, 93(3), 490-510.
Cook, S. (2000) The P versus NP Problem. Clay Mathematics Institute.
Haupt, E. (1998) Practical genetic algorithms. Wiley-Intersciencie Rewiew.
Homaifar, A. (1992) Schema analysis of the traveling salesman problem using genetic algorithms complex systems. Computer Engineering NC AT University, 6(6), 533-552.
Laporte, G. (2002) Some applications of the clustered travelling salesman problem. Journal of the Operational Research Society, (53), 972–976.
Mitchell, M. (1996) An introduction to genetic algorithms. Cambridge, Massachusetts London, England.
Sivaraj, R., Ravichandran, T., Priya, D. (2012) Solving traveling salesman problem using clustering genetic algorithm. International Journal on Computer Science and Engineering (IJCSE), 4, 1310 -1317. ISSN: 0975-3397.
Tan, P. (2006) Steinbach, M., Kumar, V. Introduction to data mining. Boston: Pearson Addison Wesley.
Tanasanne, P. (2014) Clustering evolutionary computation for solving travelling salesman problems International Journal of Advanced Computer Science and Information Technology (IJACSIT), 3, (3), 243-262, ISSN: 2296-1739
Zbigniew, M. (1994) Genetic algorithms data structures evolution programs. Springer, New York, USA.
Buthainah, F. (2008) Enhanced traveling salesman problem solving by genetic algorithm technique. World Academy of Science, Engineering and Technology, 38, 296-300.
Dantzig, G., Fulkerson R., Johnson, S. (1954) Solution of a large scale traveling salesman problem Journal of the Operations Research Society of America, 2(4), 393-410.
Chatterjee, S., Carrera, C., Lynch, L.A. (1996) Genetic algorithms and traveling salesman problem. European Journal of Operational Research, 93(3), 490-510.
Cook, S. (2000) The P versus NP Problem. Clay Mathematics Institute.
Haupt, E. (1998) Practical genetic algorithms. Wiley-Intersciencie Rewiew.
Homaifar, A. (1992) Schema analysis of the traveling salesman problem using genetic algorithms complex systems. Computer Engineering NC AT University, 6(6), 533-552.
Laporte, G. (2002) Some applications of the clustered travelling salesman problem. Journal of the Operational Research Society, (53), 972–976.
Mitchell, M. (1996) An introduction to genetic algorithms. Cambridge, Massachusetts London, England.
Sivaraj, R., Ravichandran, T., Priya, D. (2012) Solving traveling salesman problem using clustering genetic algorithm. International Journal on Computer Science and Engineering (IJCSE), 4, 1310 -1317. ISSN: 0975-3397.
Tan, P. (2006) Steinbach, M., Kumar, V. Introduction to data mining. Boston: Pearson Addison Wesley.
Tanasanne, P. (2014) Clustering evolutionary computation for solving travelling salesman problems International Journal of Advanced Computer Science and Information Technology (IJACSIT), 3, (3), 243-262, ISSN: 2296-1739
Zbigniew, M. (1994) Genetic algorithms data structures evolution programs. Springer, New York, USA.
Published
2016-07-05
How to Cite
Anaya Fuentes, G. E., Hernández Gress, E. S., & Medina Marín, J. (2016). Solución del problema del agente viajero mediante clústeres y algoritmos genéticos. Pädi Boletín Científico De Ciencias Básicas E Ingenierías Del ICBI, 4(7). https://doi.org/10.29057/icbi.v4i7.485
Section
Research papers
