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

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)1678-1684
Number of pages7
JournalTehnicki Vjesnik
Volume27
Issue number5
DOIs
StatePublished - Oct 2020
Externally publishedYes

Keywords

  • Combinatorial optimization
  • Metaheuristics
  • Set-covering problem (SCP)
  • Teaching-learning-based optimization algorithm (TLBO)

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