A Hybrid Particle Swarm Optimisation-genetic Algorithm Applied To Grid Scheduling

dc.creatorHigashino
dc.creatorWilson A.; Capretz
dc.creatorMiriam A. M.; de Toledo
dc.creatorM. Beatriz F.; Bittencourt
dc.creatorLuiz F.
dc.date2016
dc.date2017-11-13T13:24:07Z
dc.date2017-11-13T13:24:07Z
dc.date.accessioned2018-03-29T05:56:40Z
dc.date.available2018-03-29T05:56:40Z
dc.identifierInternational Journal Of Grid And Utility Computing. Inderscience Enterprises Ltd, v. 7, p. 113 - 129, 2016.
dc.identifier1741-847X
dc.identifier1741-8488
dc.identifierWOS:000385738400005
dc.identifier10.1504/IJGUC.2016.077493
dc.identifierhttp://www.inderscienceonline.com/doi/abs/10.1504/IJGUC.2016.077493
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/328237
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1365262
dc.descriptionScheduling problems have been thoroughly explored by the research community, but they acquire challenging characteristics in grid computing systems. In this context, it is important to have a scheduling strategy that can make efficient use of the available grid resources. This article focuses on the application of the particle swarm optimisation (PSO) meta-heuristic to the scheduling of independent users' jobs on grids. It is shown that the PSO method can achieve satisfactory results in simple problem instances, yet it has a tendency to stagnate around local minima in high-dimensional problems. Therefore, this research also proposes a novel hybrid particle swarm optimisation-genetic algorithm (H_PSO) method that aims to increase swarm diversity when a stagnation condition is detected. This new method is evaluated and compared with other heuristics and PSO formulations; the comparison shows that H_PSO can successfully improve the scheduling solution.
dc.description7
dc.description2
dc.description113
dc.description129
dc.languageEnglish
dc.publisherInderscience Enterprises Ltd
dc.publisherGeneva
dc.relationInternational Journal of Grid and Utility Computing
dc.rightsfechado
dc.sourceWOS
dc.subjectPso
dc.subjectParticle Swarm Optimisation
dc.subjectGrid Scheduling
dc.subjectGenetic Algorithms
dc.subjectMeta-heuristic
dc.subjectGrid Computing
dc.subjectSwarm Diversity
dc.titleA Hybrid Particle Swarm Optimisation-genetic Algorithm Applied To Grid Scheduling
dc.typeArtículos de revistas


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