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A Novel Hybrid Algorithm Based on Firefly Algorithm and Differential Evolution for Job Scheduling in Computational Grid

A Novel Hybrid Algorithm Based on Firefly Algorithm and Differential Evolution for Job Scheduling in Computational Grid

Tarun Kumar Ghosh, Sanjoy Das
Copyright: © 2018 |Volume: 9 |Issue: 2 |Pages: 15
ISSN: 1947-3532|EISSN: 1947-3540|EISBN13: 9781522545330|DOI: 10.4018/IJDST.2018040101
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MLA

Ghosh, Tarun Kumar, and Sanjoy Das. "A Novel Hybrid Algorithm Based on Firefly Algorithm and Differential Evolution for Job Scheduling in Computational Grid." IJDST vol.9, no.2 2018: pp.1-15. http://doi.org/10.4018/IJDST.2018040101

APA

Ghosh, T. K. & Das, S. (2018). A Novel Hybrid Algorithm Based on Firefly Algorithm and Differential Evolution for Job Scheduling in Computational Grid. International Journal of Distributed Systems and Technologies (IJDST), 9(2), 1-15. http://doi.org/10.4018/IJDST.2018040101

Chicago

Ghosh, Tarun Kumar, and Sanjoy Das. "A Novel Hybrid Algorithm Based on Firefly Algorithm and Differential Evolution for Job Scheduling in Computational Grid," International Journal of Distributed Systems and Technologies (IJDST) 9, no.2: 1-15. http://doi.org/10.4018/IJDST.2018040101

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Abstract

Scheduling jobs in computational Grids is considered as NP-complete problem owing to the heterogeneity of shared resources. The resources belong to many distributed administrative domains that enforce various management policies. Therefore, the use of meta-heuristics are more appropriate option in obtaining optimal results. In this article, a novel hybrid population-based global optimization algorithm, called the Hybrid Firefly Algorithm and the Differential Evolution (HFA-DE), is proposed by combining the merits of both the Firefly Algorithm and Differential Evolution. The Firefly Algorithm and the Differential Evolution are executed in parallel to support information sharing amongst the population and thus enhance searching efficiency. The proposed HFA-DE algorithm reduces the schedule makespan, processing cost, and improves resource utilization. The HFA-DE is compared with the standard Firefly Algorithm, the Differential Evolution and the Particle Swarm Optimization algorithms on all these parameters. The comparison results exhibit that the proposed algorithm outperforms the other three algorithms.

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