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Exploring Initialization Strategies for Metaheuristic Optimization: Case Study of the Set-Union Knapsack Problem

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dc.contributor.authorGarcía, José
dc.contributor.authorLeiva-Araos, Andrés
dc.contributor.authorCrawford, Broderick
dc.contributor.authorSoto, Ricardo
dc.contributor.authorPinto, Hernán
dc.date.accessioned2023-08-02T16:28:46Z
dc.date.available2023-08-02T16:28:46Z
dc.date.issued2023
dc.description.abstractIn recent years, metaheuristic methods have shown remarkable efficacy in resolving complex combinatorial challenges across a broad spectrum of fields. Nevertheless, the escalating complexity of these problems necessitates the continuous development of innovative techniques to enhance the performance and reliability of these methods. This paper aims to contribute to this endeavor by examining the impact of solution initialization methods on the performance of a hybrid algorithm applied to the set union knapsack problem (SUKP). Three distinct solution initialization methods, random, greedy, and weighted, have been proposed and evaluated. These have been integrated within a sine cosine algorithm employing k-means as a binarization procedure. Through testing on medium- and large-sized SUKP instances, the study reveals that the solution initialization strategy influences the algorithm’s performance, with the weighted method consistently outperforming the other two. Additionally, the obtained results were benchmarked against various metaheuristics that have previously solved SUKP, showing favorable performance in this comparison.
dc.description.versionVersión publicada
dc.identifier.citationGarcía, J.; Leiva-Araos, A.; Crawford, B; Soro, R; Pinto, H. Exploring Initialization Strategies for Metaheuristic Optimization: Case Study of the Set-Union Knapsack Problem. Mathematics 2023, 11, 2695. https://doi.org/10.3390/ math11122695
dc.identifier.doihttps://doi.org/10.3390/math11122695
dc.identifier.urihttps://repositorio.udd.cl/handle/11447/7823
dc.language.isoen
dc.subjectCombinatorial optimization
dc.subjectMachine learning
dc.subjectMetaheuristics
dc.subjectSet-union knapsack problem
dc.subjectInitialization operators
dc.titleExploring Initialization Strategies for Metaheuristic Optimization: Case Study of the Set-Union Knapsack Problem
dc.typeArticle
dcterms.accessRightsAcceso abierto
dcterms.sourceMathematics
dspace.entity.typePublication

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