El problema del agente viajero resuelto mediante agrupación en clústeres y algoritmos genéticos

Palabras clave: Agente viajero, algoritmos genéticos, agrupación, recorrido total, metaheurísticos

Resumen

    El presente artículo encuentra soluciones factibles para el Problema del Agente Viajero, mediante una nueva forma de agrupar al problema en clústeres con la intención de crear subproblemas del Agente Viajero, las cuales se resuelven por el metaheurístico algoritmos genéticos. Posteriormente las agrupaciones son unidas nuevamente utilizando las soluciones proporcionadas por el metaheurístico, obteniendo una solución final, además, la propuesta de agrupación de ciudades consiste en la utilización de la media aritmética sobre las coordenadas, para calcular iterativamente a los nodos representativos de cada familia. En la literatura se encuentra una tendencia para abordar este problema mediante la metodología propuesta. Los resultados demuestran que al utilizar esta metodología de agrupación se mejoran los resultados en comparación a las soluciones algoritmos genéticos sin utilizar clústeres.

Descargas

La descarga de datos todavía no está disponible.

Citas

Anaya, G.E., Hernández, E.S., Seck, J.C., and Medina J., 2016. Solución al Problema de Secuenciación de Trabajos mediante el Problema del Agente Viajero, Revista Iberoamericana de Automática e Informática Industrial 13, 430–437. DOI:10.1016/j.riai.2016.07.003

Anaya, G.E., Hernández, E.S., Tuoh, J.C., Medina, J., 2018. Solution to travelling salesman problem by clusters and a modified multi-restart iterated local search metaheuristic. PLOS ONE, 1-20. DOI:10.1371/journal.pone.0201868

Baniasadi, P., Foumani, M., Smith-Miles, K., Ejov, V. 2020. A transformation technique for the clustered generalized traveling salesman problem with applications to logistics. European Journal of Operational Research, 285(2), 444-457. DOI: 10.1016/j.ejor.2020.01.053

Barros, A., Jabba, D., Ardila, C., Guzman, L., Ruiz, J. 2021. Adaptation of Parallel Framework to Solve Traveling Salesman Problem Using Genetic Algorithms and Tabu Search. Artificial Intelligence, 19.

Brito, D., Marin, L., y Ramírez, H., 2018. Ciclos Hamiltonianos que pasan a través de un bosque lineal en grafos bipartitos balanceados. Revista de matemática: Teoría y aplicaciones 25, 349-367. DOI: https://doi.org/10.15517/rmta.v25i2.33908

Campuzano, G., Obreque, C., Aguayo, M. 2020. Accelerating the Miller–Tucker–Zemlin model for the asymmetric traveling salesman problem. Expert Systems with Applications, 148, 113229. https://doi.org/10.1016/j.eswa.2020.113229

Dahiya, C., Sangwan, S., 2018. Literature Review on Traveling Salesman Problem. International Journal of Research 05, 1152-1155. DOI: https://journals.pen2print.org/index.php/ijr/article/view/15490/15018

Dantzig, G., Fulkerson, R., Johnson, S., 1954. Solution of a large-scale traveling salesman problem. Operations Research 2, 393-410.

Dutta, S., Bhattacharya, S.,A., 2015. Short review of clustering techniques. International Journal of Advanced Research in Management and Social Sciences, 131–139.

Feliciano, A., Cuevas, V., Severino F., 2011. Geometría analítica. Ed. UAI de la UAGro. ISBN 978-607-00-4156-3.

Hernández J., Hernández, S., Jiménez, J., Hernández, M., Hernández, I., 2017. Enfoque híbrido metaheurístico AG-RS para el problema de asignación del buffer que minimiza el inventario en proceso en líneas de producción abiertas en serie. Revista Iberoamericana de Automática e Informática Industrial 16, 447-458. DOI: 10.4995/riai.2017.10883

Hussain, A., Muhammad, Y., Sajid, M., Hussain, I., Shoukry, A., Gani, S., 2017. Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator. Computational Intelligence and Neuroscience, 1-7. DOI: 10.1155/2017/7430125

Kaabi, J., Harrath, Y., 2019. Permutation rules and genetic algorithm to solve the traveling salesman problem. Arab Journal of Basic and Applied Sciences 26, 283-291. DOI: 10.1080/25765299.2019.1615172

Kaur, G., Singh, D., Kaur, S., 2014. Pollination based optimitation for feature reduction at feature level fusion of speech and signature biometrics. IEEE, 1-6. DOI: 10.1109/ICRITO.2014.7014771

Kaur, N., Kaur J., 2012. Efficient k-means clustering algorithm using ranking method in data mining. International Journal of Advanced Research in Computer Engineering and Technology 1, 85–91.

Li, X., Gao, S., 2016. Cloud-Resolving Modeling of Convective. Springer, DOI:10.1007/978-3-319-26360-1

Liu, G., Song, S., Wu, C., 2012. Two Techniques to Improve the NEH Algorithm for Flow-Shop Scheduling Problems. Advanced Intelligent Computing Theories and Applications with Aspects of Artificial Intelligence of the series Lecture Notes in Computer Science 68, 41–48. DOI: 10.1007/978-3-642-25944-9_6

Liu, Y., Dong, H., Lohse, N., and Petrovic, S., 2016. A Multi-objective Genetic Algorithm for Optimisation of Energy Consumption and Shop Floor Production Performance. International Journal of Production Economics 179, 259-272. DOI:10.1016/j.ijpe.2016.06.019

Mosayebi, M., Sodhi, M., Wettergren, T. A. (2021). The Traveling Salesman Problem with Job-times (TSPJ). Computers & Operations Research, 129, 105226. DOI: https://doi.org/10.1016/j.cor.2021.105226.

Nadana, T., Shriram, R., 2014. Metadata based Clustering Model for Data Mining. Journal of Theoretical and Applied Information Technology, 59–64.

Nagy, M., Negru, D., 2014. Using clustering software for exploring spatial and temporal patterns in non-communicable diseases. European Scientific Journal 10, 37-47. DOI: 10.19044/esj.2014.v10n33p%25p

Nidhi, S. A., 2015. Modified Approach for Incremental k-Means Clustering Algorithm, 3, 1081–1084.

Nizam, M., 2010. Kohoen Neural Network Clustering for Voltage Control in Power Systems. Terakreditasi 51, 115-122.

Panwar, K., Deep, K. 2021. Discrete Grey Wolf Optimizer for symmetric travelling salesman problem. Applied Soft Computing, 105, 107298. DOI: 10.1016/j.asoc.2021.107298

Rani, S., Kholidah, K., Huda, S., 2018. A development of travel itinerary planning application using traveling salesman problem and k-means clustering approach. 7th International Conference on Software and Computer Applications, 327-331. DOI: https://doi.org/10.1145/3185089.3185142

Refianti, R., Mutiara, A., Juarna, A., Ikhsan, S., 2014. Analysis Implementation of algorithm clustering affnity propagation and k-means at data student based on gpa and duration of bachelor-thesis completion. Journal of Theoretical and Applied Information Technology 35, 69–76.

Saroj, Chaudhary, T., 2015. Study on Various Clustering Techniques. International Journal of Computer Science and Information Technologies 6, 3031-3033.

Sivaraj, R., Ravichandran, T., 2011. An Improved Clustering Based Genetic Algorithm for Solving Complex NP Problems. Journal of Computer Science 7, 1033-1037.

Tavse P., Khandelwal A., 2014. A critical Review on Data Clustering in Wireless Network. International Journal of Advanced Computer Research 4, 795–798.

TSPLIB, 2020. Base de datos Traveling Salesman Problem Library. http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/

Vijayalakshmi, S., Jayanavithraa, C., Ramya, L., 2013. Gene Expression Data Analysis Using Automatic Spectral MEQPSO Clustering Algorithm. International Journal of Advanced Research in Computer and Communication Engineering 2, 1145-1148.

Vitayasak, S., Pongcharoen, P., Hicks, C., 2017. A tool for solving stochastic facility layout problems with stochastic demand using either a Genetic Algorithm or modified Backtracking Search Algorithm. International Journal of Production Economics 190, 146-157. DOI: 10.1016/j.ijpe.2016.03.019.

Weiya, R., Guohui, L., Dan, T., 2015. Graph clustering by congruency approximation. The institution of Engineering and Technology, 841–849. DOI: 10.1049/iet-cvi.2014.0131

Publicado
2021-07-05
Cómo citar
Anaya-Fuentes, G. E. (2021). El problema del agente viajero resuelto mediante agrupación en clústeres y algoritmos genéticos. Pädi Boletín Científico De Ciencias Básicas E Ingenierías Del ICBI, 9(17), 88-97. https://doi.org/10.29057/icbi.v9i17.7130