Technical gazette, Vol. 27 No. 5, 2020.
Preliminary communication
https://doi.org/10.17559/TV-20180501230511
A Teaching-Learning-Based Optimization Algorithm for the Weighted Set-Covering Problem
Broderick Crawford
orcid.org/0000-0001-5500-0188
; Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso, Chile
Ricardo Soto
; Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso, Chile
Wenceslao Palma
; Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso, Chile
Felipe Aballay
; Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso, Chile
Gino Astorga*
orcid.org/0000-0001-5500-0188
; Universidad de Valparaíso, Prat 856, Valparaíso, Chile
José Lemus-Romani
; Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso, Chile
Sanjay Misra
; 1) Department of Computer and Information Science, Covenant University, Nigeria, KM 10 Idiroko Rd, Ota, Nigeria; 2) Atilim University, 06836 Incek/Ankara Turkey
Carlos Castro
; Universidad Técnica Federico Santa María, Avenida España 1680, Valparaíso, Chile
Fernando Paredes
; Escuela de Ingeniería Industrial, Universidad Diego Portales, Manuel Rodríguez Sur 415, Santiago, Chile
José-Miguel Rubio
; Universidad Bernardo O'Higgins, Av. Viel 1497, Santiago, Chile
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.
Keywords
combinatorial optimization; metaheuristics; set-covering problem (SCP); teaching-learning-based optimization algorithm (TLBO)
Hrčak ID:
244863
URI
Publication date:
17.10.2020.
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