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A 2-level metaheuristic for the set covering problem

dc.contributor.authorValenzuela C.
dc.contributor.authorCrawford B.
dc.contributor.authorSoto R.
dc.contributor.authorMonfroy E.
dc.contributor.authorParedes F.
dc.date.accessioned2020-09-02T22:29:53Z
dc.date.available2020-09-02T22:29:53Z
dc.date.issued2012
dc.identifier10.15837/ijccc.2012.2.1417
dc.identifier.citation7, 2, 377-387
dc.identifier.issn18419836
dc.identifier.urihttps://hdl.handle.net/20.500.12728/6497
dc.descriptionMetaheuristics are solution methods which combine local improvement procedures and higher level strategies for solving combinatorial and nonlinear optimization problems. In general, metaheuristics require an important amount of effort focused on parameter setting to improve its performance. In this work a 2-level metaheuristic approach is proposed so that Scatter Search and Ant Colony Optimization act as "low level" metaheuristics, whose parameters are set by a "higher level" Genetic Algorithm during execution, seeking to improve the performance and to reduce the maintenance. The Set Covering Problem is taken as reference since is one of the most important optimization problems, serving as basis for facility location problems, airline crew scheduling, nurse scheduling, and resource allocation. © 2006-2012 by CCC Publications.
dc.language.isoen
dc.publisherAgora University
dc.subjectAnt colony optimization
dc.subjectGenetic algorithm
dc.subjectMetaheuristics
dc.subjectScatter search
dc.subjectSet covering problem
dc.titleA 2-level metaheuristic for the set covering problem
dc.typeArticle


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