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dc.contributor.authorYounis, Muhanad Tahriren
dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2018-05-31T07:21:54Z
dc.date.available2018-05-31T07:21:54Z
dc.date.issued2018-05-26
dc.identifier.citationYounis, M.T and Yang, S. (2018) Hybrid meta-heuristic algorithms for independent job scheduling in grid computing. Applied Soft Computing, in press,en
dc.identifier.issn1568-4946
dc.identifier.urihttp://hdl.handle.net/2086/16243
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractThe term ’grid computing’ is used to describe an infrastructure that connects geographically distributed computers and heterogeneous platforms owned by multiple organizations allowing their computational power, storage capabilities and other resources to be selected and shared. The job scheduling problem is recognized as being one of the most important and challenging issues in grid computing environments. This paper proposes two strongly coupled hybrid meta-heuristic schedulers. The first scheduler combines Ant Colony Optimisation and Variable Neighbourhood Search in which the former acts as the primary algorithm which, during its execution, calls the latter as a supporting algorithm, while the second merges the Genetic Algorithm with Variable Neighbourhood Search in the same fashion. Several experiments were carried out to analyse the performance of the proposed schedulers in terms of minimizing the makespan using well known benchmarks. The experiments show that the proposed schedulers achieved impressive results compared to other selected approaches from the bibliography.en
dc.language.isoen_USen
dc.publisherElsevieren
dc.subjectHybrid meta-heuristicen
dc.subjectAnt colony optimizationen
dc.subjectGenetic algorithmen
dc.subjectVariable neighbourhood searchen
dc.subjectJob schedulingen
dc.titleHybrid meta-heuristic algorithms for independent job scheduling in grid computingen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1016/j.asoc.2018.05.032
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceN/Aen
dc.date.acceptance2018-05-21en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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