A meta-optimization approach for covering problems in facility location

BRODERICK CRAWFORD LABRIN, RICARDO JAVIER SOTO DE GIORGIS, Eric Monfroy, Gino Astorga, José García, Enrique Cortes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

21 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 languageEnglish
Title of host publicationApplied Computer Sciences in Engineering - 4th Workshop on Engineering Applications, WEA 2017, Proceedings
EditorsJuan Carlos Figueroa-Garcia, Eduyn Ramiro Lopez-Santana, Roberto Ferro-Escobar, Jose Luis Villa-Ramirez
PublisherSpringer Verlag
Pages565-578
Number of pages14
ISBN (Print)9783319669625
DOIs
StatePublished - 1 Jan 2017
Event4th Workshop on Engineering Applications, WEA 2017 - Cartagena, Colombia
Duration: 27 Sep 201729 Sep 2017

Publication series

NameCommunications in Computer and Information Science
Volume742
ISSN (Print)1865-0929

Conference

Conference4th Workshop on Engineering Applications, WEA 2017
CountryColombia
CityCartagena
Period27/09/1729/09/17

Keywords

  • Artificial bee colony algorithm
  • Covering problems
  • Facility location
  • Swarm intelligence

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