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

Broderick Crawford; Ricardo Soto; Wenceslao Palma; Felipe Aballay; Gino Astorga; José Lemus-Romani; Sanjay Misra; Carlos Castro; Fernando Paredes; José Miguel Rubio

doi:10.17559/TV-20180501230511

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

Broderick Crawford, Ricardo Soto, Wenceslao Palma, Felipe Aballay, Gino Astorga, José Lemus-Romani, Sanjay Misra, Carlos Castro, Fernando Paredes, José Miguel Rubio

SCHOOL OF COMPUTER ENGINEERING

Research output: Contribution to journal › Article › peer-review

2 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 language	English
Pages (from-to)	1678-1684
Number of pages	7
Journal	Tehnicki Vjesnik
Volume	27
Issue number	5
DOIs	https://doi.org/10.17559/TV-20180501230511
State	Published - Oct 2020

Keywords

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

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10.17559/TV-20180501230511

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title = "A teaching-learning-based optimization algorithm for the weighted set-covering problem",

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.",

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year = "2020",

month = oct,

doi = "10.17559/TV-20180501230511",

language = "English",

volume = "27",

pages = "1678--1684",

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AU - Crawford, Broderick

AU - Soto, Ricardo

AU - Palma, Wenceslao

AU - Aballay, Felipe

AU - Astorga, Gino

AU - Lemus-Romani, José

AU - Misra, Sanjay

AU - Castro, Carlos

AU - Paredes, Fernando

AU - Rubio, José Miguel

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)

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

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Keywords

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