TY - JOUR
T1 - A teaching-learning-based optimization algorithm for the weighted set-covering problem
AU - CRAWFORD LABRIN, BRODERICK
AU - SOTO DE GIORGIS, RICARDO JAVIER
AU - PALMA MUÑOZ, WENCESLAO ENRIQUE
AU - Aballay, Felipe
AU - Astorga, Gino
AU - Lemus-Romani, José
AU - Misra, Sanjay
AU - Castro, Carlos
AU - Paredes, Fernando
AU - Rubio, José Miguel
N1 - Publisher Copyright:
© 2020, Strojarski Facultet. All rights reserved.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
KW - Combinatorial optimization
KW - Metaheuristics
KW - Set-covering problem (SCP)
KW - Teaching-learning-based optimization algorithm (TLBO)
UR - http://www.scopus.com/inward/record.url?scp=85092637629&partnerID=8YFLogxK
U2 - 10.17559/TV-20180501230511
DO - 10.17559/TV-20180501230511
M3 - Article
AN - SCOPUS:85092637629
VL - 27
SP - 1678
EP - 1684
JO - Tehnicki Vjesnik
JF - Tehnicki Vjesnik
SN - 1330-3651
IS - 5
ER -