A meta-optimization approach for covering problems in facility location

Broderick Crawford; Ricardo Soto; Eric Monfroy; Gino Astorga; José García; Enrique Cortes

doi:10.1007/978-3-319-66963-2_50

A meta-optimization approach for covering problems in facility location

Broderick Crawford, Ricardo Soto, Eric Monfroy, Gino Astorga, José García, Enrique Cortes

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

28 Scopus citations

Abstract

In this paper, we solve the Set Covering Problem with a meta-optimization approach. One of the most popular models among facility location models is the Set Covering Problem. The meta-level metaheuristic operates on solutions representing the parameters of other metaheuristic. This approach is applied to an Artificial Bee Colony metaheuristic that solves the non-unicost set covering. The Artificial Bee Colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. This metaheuristic owns a parameter set with a great influence on the effectiveness of the search. These parameters are fine-tuned by a Genetic Algorithm, which trains the Artificial Bee Colony metaheuristic by using a portfolio of set covering problems. The experimental results show the effectiveness of our approach which produces very near optimal scores when solving set covering instances from the OR-Library.

Original language	English
Title of host publication	Applied Computer Sciences in Engineering - 4th Workshop on Engineering Applications, WEA 2017, Proceedings
Editors	Juan Carlos Figueroa-Garcia, Eduyn Ramiro Lopez-Santana, Roberto Ferro-Escobar, Jose Luis Villa-Ramirez
Publisher	Springer Verlag
Pages	565-578
Number of pages	14
ISBN (Print)	9783319669625
DOIs	https://doi.org/10.1007/978-3-319-66963-2_50
State	Published - 2017
Event	4th Workshop on Engineering Applications, WEA 2017 - Cartagena, Colombia Duration: 27 Sep 2017 → 29 Sep 2017

Publication series

Name	Communications in Computer and Information Science
Volume	742
ISSN (Print)	1865-0929

Conference

Conference	4th Workshop on Engineering Applications, WEA 2017
Country/Territory	Colombia
City	Cartagena
Period	27/09/17 → 29/09/17

Keywords

Artificial bee colony algorithm
Covering problems
Facility location
Swarm intelligence

Access to Document

10.1007/978-3-319-66963-2_50

Cite this

Crawford, B., Soto, R., Monfroy, E., Astorga, G., García, J., & Cortes, E. (2017). A meta-optimization approach for covering problems in facility location. In J. C. Figueroa-Garcia, E. R. Lopez-Santana, R. Ferro-Escobar, & J. L. Villa-Ramirez (Eds.), Applied Computer Sciences in Engineering - 4th Workshop on Engineering Applications, WEA 2017, Proceedings (pp. 565-578). (Communications in Computer and Information Science; Vol. 742). Springer Verlag. https://doi.org/10.1007/978-3-319-66963-2_50

Crawford, Broderick ; Soto, Ricardo ; Monfroy, Eric et al. / A meta-optimization approach for covering problems in facility location. Applied Computer Sciences in Engineering - 4th Workshop on Engineering Applications, WEA 2017, Proceedings. editor / Juan Carlos Figueroa-Garcia ; Eduyn Ramiro Lopez-Santana ; Roberto Ferro-Escobar ; Jose Luis Villa-Ramirez. Springer Verlag, 2017. pp. 565-578 (Communications in Computer and Information Science).

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abstract = "In this paper, we solve the Set Covering Problem with a meta-optimization approach. One of the most popular models among facility location models is the Set Covering Problem. The meta-level metaheuristic operates on solutions representing the parameters of other metaheuristic. This approach is applied to an Artificial Bee Colony metaheuristic that solves the non-unicost set covering. The Artificial Bee Colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. This metaheuristic owns a parameter set with a great influence on the effectiveness of the search. These parameters are fine-tuned by a Genetic Algorithm, which trains the Artificial Bee Colony metaheuristic by using a portfolio of set covering problems. The experimental results show the effectiveness of our approach which produces very near optimal scores when solving set covering instances from the OR-Library.",

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author = "Broderick Crawford and Ricardo Soto and Eric Monfroy and Gino Astorga and Jos{\'e} Garc{\'i}a and Enrique Cortes",

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Crawford, B , Soto, R, Monfroy, E, Astorga, G, García, J & Cortes, E 2017, A meta-optimization approach for covering problems in facility location. in JC Figueroa-Garcia, ER Lopez-Santana, R Ferro-Escobar & JL Villa-Ramirez (eds), Applied Computer Sciences in Engineering - 4th Workshop on Engineering Applications, WEA 2017, Proceedings. Communications in Computer and Information Science, vol. 742, Springer Verlag, pp. 565-578, 4th Workshop on Engineering Applications, WEA 2017, Cartagena, Colombia, 27/09/17. https://doi.org/10.1007/978-3-319-66963-2_50

A meta-optimization approach for covering problems in facility location. / Crawford, Broderick ; Soto, Ricardo; Monfroy, Eric et al.
Applied Computer Sciences in Engineering - 4th Workshop on Engineering Applications, WEA 2017, Proceedings. ed. / Juan Carlos Figueroa-Garcia; Eduyn Ramiro Lopez-Santana; Roberto Ferro-Escobar; Jose Luis Villa-Ramirez. Springer Verlag, 2017. p. 565-578 (Communications in Computer and Information Science; Vol. 742).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - A meta-optimization approach for covering problems in facility location

AU - Crawford, Broderick

AU - Soto, Ricardo

AU - Monfroy, Eric

AU - Astorga, Gino

AU - García, José

AU - Cortes, Enrique

PY - 2017

Y1 - 2017

N2 - In this paper, we solve the Set Covering Problem with a meta-optimization approach. One of the most popular models among facility location models is the Set Covering Problem. The meta-level metaheuristic operates on solutions representing the parameters of other metaheuristic. This approach is applied to an Artificial Bee Colony metaheuristic that solves the non-unicost set covering. The Artificial Bee Colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. This metaheuristic owns a parameter set with a great influence on the effectiveness of the search. These parameters are fine-tuned by a Genetic Algorithm, which trains the Artificial Bee Colony metaheuristic by using a portfolio of set covering problems. The experimental results show the effectiveness of our approach which produces very near optimal scores when solving set covering instances from the OR-Library.

AB - In this paper, we solve the Set Covering Problem with a meta-optimization approach. One of the most popular models among facility location models is the Set Covering Problem. The meta-level metaheuristic operates on solutions representing the parameters of other metaheuristic. This approach is applied to an Artificial Bee Colony metaheuristic that solves the non-unicost set covering. The Artificial Bee Colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. This metaheuristic owns a parameter set with a great influence on the effectiveness of the search. These parameters are fine-tuned by a Genetic Algorithm, which trains the Artificial Bee Colony metaheuristic by using a portfolio of set covering problems. The experimental results show the effectiveness of our approach which produces very near optimal scores when solving set covering instances from the OR-Library.

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KW - Swarm intelligence

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T3 - Communications in Computer and Information Science

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BT - Applied Computer Sciences in Engineering - 4th Workshop on Engineering Applications, WEA 2017, Proceedings

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A2 - Ferro-Escobar, Roberto

A2 - Villa-Ramirez, Jose Luis

PB - Springer Verlag

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Y2 - 27 September 2017 through 29 September 2017

ER -

Crawford B , Soto R, Monfroy E, Astorga G, García J, Cortes E. A meta-optimization approach for covering problems in facility location. In Figueroa-Garcia JC, Lopez-Santana ER, Ferro-Escobar R, Villa-Ramirez JL, editors, Applied Computer Sciences in Engineering - 4th Workshop on Engineering Applications, WEA 2017, Proceedings. Springer Verlag. 2017. p. 565-578. (Communications in Computer and Information Science). doi: 10.1007/978-3-319-66963-2_50

A meta-optimization approach for covering problems in facility location

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