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Optimal job scheduling in grid computing using efficient binary artificial bee colony optimization

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Abstract

The artificial bee colony has the advantage of employing fewer control parameters compared with other population-based optimization algorithms. In this paper a binary artificial bee colony (BABC) algorithm is developed for binary integer job scheduling problems in grid computing. We further propose an efficient binary artificial bee colony extension of BABC that incorporates a flexible ranking strategy (FRS) to improve the balance between exploration and exploitation. The FRS is introduced to generate and use new solutions for diversified search in early generations and to speed up convergence in latter generations. Two variants are introduced to minimize the makepsan. In the first a fixed number of best solutions is employed with the FRS while in the second the number of the best solutions is reduced with each new generation. Simulation results for benchmark job scheduling problems show that the performance of our proposed methods is better than those alternatives such as genetic algorithms, simulated annealing and particle swarm optimization.

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Acknowledgments

The authors would like to thank Zhihua Cui for his scientific collaboration in this research work. This work is supported partly by the Kangwon National University, the National Natural Science Foundation of China (Grant No. 61173035, 61105117), the Fundamental Research Funds for the Central Universities (Grant No. 2012TD027), the Program for New Century Excellent Talents in University (Grant No. NCET-11-0861), and the Dalian Science and Technology Fund (Grant No. 2010J21DW006).

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Authors and Affiliations

  1. Department of Industrial Engineering, Kangwon National University, Chunchon, 200-701, Korea

    Sung-Soo Kim & Ji-Hwan Byeon

  2. School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China

    Hongbo Liu

  3. Institute for Neural Computation, University of California San Diego, La Jolla, CA, 92093, USA

    Hongbo Liu

  4. Machine Intelligence Research Labs, Scientific Network for Innovation and Research Excellence, Auburn, WA, 98071, USA

    Ajith Abraham

  5. IT For Innovations - Center of Excellence, VSB - Technical University of Ostrava, Ostrava - Poruba, 708 33, Czech Republic

    Ajith Abraham

  6. Department of Electronic Engineering, National University of Ireland Maynooth, Maynooth, Kildare, Ireland

    Seán McLoone

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  1. Sung-Soo Kim
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Correspondence to Hongbo Liu.

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Communicated by F. Herrera.

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Kim, SS., Byeon, JH., Liu, H. et al. Optimal job scheduling in grid computing using efficient binary artificial bee colony optimization. Soft Comput 17, 867–882 (2013). https://doi.org/10.1007/s00500-012-0957-7

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