A hybrid particle swarm optimisation-genetic algorithm applied to grid scheduling. (2016)

• Record Type: Journal Article
• Authors: Higashino, Wilson A.; Capretz, Miriam A.M.; Toledo, M. Beatriz F. De; Bittencourt, Luiz F.
• Is Part Of: International journal of grid and utility computing. Volume 7:Number 2(2016)
• Page Start: 113
• Page End: 129
• Publication Date: 2016
• Subjects: PSO -- particle swarm optimisation -- grid scheduling -- genetic algorithms -- metaheuristics -- grid computing -- swarm diversity -- stagnation; Electronic data processing -- Distributed processing -- Periodicals; Electronic commerce -- Management -- Computer programs -- Periodicals; 004.605
• Languages: English
• ISSNs: 1741-847X

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.