A hybrid particle swarm optimisation-genetic algorithm applied to grid scheduling Online publication date: Mon, 04-Jul-2016
by Wilson A. Higashino; Miriam A.M. Capretz; M. Beatriz F. De Toledo; Luiz F. Bittencourt
International Journal of Grid and Utility Computing (IJGUC), Vol. 7, No. 2, 2016
Abstract: Scheduling 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.
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