Traveling Salesman Problem solved by clustering and genetic algorithms
This article seeks to find feasible solutions for the Traveling Salesman Problem, by means of a new way of grouping the problem into clusters with the intention of creating Traveling Salesman subproblems, which are proposed to be solved by the metaheuristic genetic algorithm. Subsequently, the group...
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | México |
| Institución: | UNIVERSIDAD AUTÓNOMA DEL ESTADO DE HIDALGO |
| Repositorio: | PÄDI Boletín Científico de Ciencias Básicas e Ingeniería del ICBI |
| Idioma: | español |
| OAI Identifier: | oai:repository.uaeh.edu.mx:article/7130 |
| Acceso en línea: | https://repository.uaeh.edu.mx/revistas/index.php/icbi/article/view/7130 |
| Access Level: | acceso abierto |
| Palabra clave: | Traveling Salesman genetic algorithms clusters fitness metaheuristics Agente viajero algoritmos genéticos agrupación recorrido total metaheurísticos |
| Sumario: | This article seeks to find feasible solutions for the Traveling Salesman Problem, by means of a new way of grouping the problem into clusters with the intention of creating Traveling Salesman subproblems, which are proposed to be solved by the metaheuristic genetic algorithm. Subsequently, the groupings are joined again using the solutions provided by the metaheuristic, obtaining a final solution. In addition, a way of grouping the cities of the problem is proposed using the arithmetic mean of the coordinates to iteratively calculate the representative nodes of each family. In the literature there is no similar method to solve the problem in question. The results show that using this grouping methodology improves the results compared to the genetic algorithms solutions without using clusters. |
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