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dc.contributor.authorYounis, Muhanad Tahriren
dc.contributor.authorYang, Shengxiangen
dc.contributor.authorPassow, Benjamin N.en
dc.date.accessioned2018-04-11T10:14:28Z
dc.date.available2018-04-11T10:14:28Z
dc.date.issued2018-03
dc.identifier.citationYounis, M. T, Yang, S. and Passow, B. N. (2018), 'A loosely coupled hybrid meta-heuristic algorithm for the static independent task scheduling problem in grid computing'. 2018 IEEE Congress on Evolutionary Computation (CEC). Barra da Tijuca, Rio de Janeiro, Brazil , 8-13 July.en
dc.identifier.urihttp://hdl.handle.net/2086/15951
dc.description.abstractTask scheduling is one of the most difficult problems in grid computing systems. Therefore, various studies have been proposed to present methods which provide efficient schedules. Meta-heuristic approaches are among the methods which have proven their efficiency in this domain. However, the literature shows that hybridizing two or more meta-heuristics can improve performance to a greater extent than stand-alone algorithms as the new high-level algorithm will inherit the best features of the hybridized algorithms. In this paper, a loosely coupled hybrid meta-heuristic algorithm is proposed for solving the static independent task scheduling problem in grid computing. It combines ant colony optimization and variable neighborhood search, where the former operates first and whose output is subsequently improved by the latter. The experimental results show that the proposed algorithm achieves better task-machine mapping in terms of minimizing makespan than other selected approaches from the literature.en
dc.language.isoen_USen
dc.publisherIEEE Pressen
dc.subjectHybrid meta-heuristicen
dc.subjectAnt Colony Optimizationen
dc.subjectVariable Neighborhood Searchen
dc.subjectTask Schedulingen
dc.titleA loosely coupled hybrid meta-heuristic algorithm for the static independent task scheduling problem in grid computingen
dc.typeConferenceen
dc.identifier.doihttps://doi.org/10.1109/cec.2018.8477765
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceN/Aen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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