A teaching-learning-based optimization algorithm for the weighted set-covering problem

BRODERICK CRAWFORD LABRIN, RICARDO JAVIER SOTO DE GIORGIS, WENCESLAO ENRIQUE PALMA MUÑOZ, Felipe Aballay, Gino Astorga, José Lemus-Romani, Sanjay Misra, Carlos Castro, Fernando Paredes, José Miguel Rubio

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

1 Cita (Scopus)

Resumen

The need to make good use of resources has allowed metaheuristics to become a tool to achieve this goal. There are a number of complex problems to solve, among which is the Set-Covering Problem, which is a representation of a type of combinatorial optimization problem, which has been applied to several real industrial problems. We use a binary version of the optimization algorithm based on teaching and learning to solve the problem, incorporating various binarization schemes, in order to solve the binary problem. In this paper, several binarization techniques are implemented in the teaching/learning based optimization algorithm, which presents only the minimum parameters to be configured such as the population and number of iterations to be evaluated. The performance of metaheuristic was evaluated through 65 benchmark instances. The results obtained are promising compared to those found in the literature.

Idioma originalInglés
Páginas (desde-hasta)1678-1684
Número de páginas7
PublicaciónTehnicki Vjesnik
Volumen27
N.º5
DOI
EstadoPublicada - oct. 2020
Publicado de forma externa

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