A genetic algorithm to solve 3D traveling salesman problem with initial population based on a GRASP algorithm

Sebastian Meneses, Rony Cueva, Manuel Tupia, Miguel Guanira

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

5 Citas (Scopus)

Resumen

In this paper, the problem of obtaining optimal routes on tridimensional environments is discussed. This scenario is called as Traveler Salesman Problem (TSP 3D-variation). As is widely known, TSP has NP-complexity so is necessary to apply techniques to solve it approximately (no exacts solutions available). The purpose of this research is to present a genetic algorithm to solve 3D-TSP variation. These kind of evolutionary algorithms are ideal for solving complex problems where necessary rearrangements and route optimization. In case of genetic algorithms, optimal solutions appear faster depending on the quality of initial population, so theory recommends using metaheuristics for generating this population. In this study, it has used a metaheuristic GRASP algorithm to generate the initial population and, over it, apply the genetic operators proposed for optimizing individuals obtained. The results have optimal routes of movement and displacement and are directly applicable in the storage industry.

Idioma originalInglés
Páginas (desde-hasta)S1-S10
PublicaciónJournal of Computational Methods in Sciences and Engineering
Volumen17
N.ºS1
DOI
EstadoPublicada - 2017

Huella

Profundice en los temas de investigación de 'A genetic algorithm to solve 3D traveling salesman problem with initial population based on a GRASP algorithm'. En conjunto forman una huella única.

Citar esto