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A Hybrid Artificial Bee Colony Algorithm to Solve Multi-objective Hybrid Flowshop in Cloud Computing Systems

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Cloud Computing and Security (ICCCS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10602))

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Abstract

This paper proposes a local search enhanced hybrid artificial bee colony algorithm (LABC) for solving the multi-objective flexible task scheduling problem in Cloud computing system. The task scheduling is modeled as a hybrid flow shop scheduling (HFS) problem. In multiple objectives HFS problems, three objectives, i.e., minimum of the makespan, maximum workload, and total workload are considered simultaneously. In the proposed algorithm, each solution is represented as an integer string. A deep-exploitation function is developed, which is used by the onlooker bee and the best food source found so far to complete a deep level of search. The proposed algorithm is tested on sets of the well-known benchmark instances. Through the analysis of experimental results, the highly effective performance of the proposed LABC algorithm is shown against several efficient algorithms from the literature.

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Acknowledgments

This research is partially supported by National Science Foundation of China under Grant 61573178, 61374187, 61603169 and 61503170, basic scientific research foundation of Northeastern University under Grant N110208001, starting foundation of Northeastern University under Grant 29321006, Science Foundation of Liaoning Province in China (2013020016), Key Laboratory Basic Research Foundation of Education Department of Liaoning Province (LZ2014014), Shandong Province Higher Educational Science and Technology Program (J14LN28), Postdoctoral Science Foundation of China (2015T80798, 2014M552040), and State Key Laboratory of Synthetical Automation for Process Industries (PAL-N201602).

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Authors and Affiliations

  1. College of Computer Science, Liaocheng University, Liaocheng, 252059, People’s Republic of China

    Jun-qing Li, Yu-yan Han & Cun-gang Wang

  2. State Key Laboratory of Synthetic Automation for Process Industries, Northeastern University, Shenyang, 110819, People’s Republic of China

    Jun-qing Li

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  1. Jun-qing Li
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  2. Yu-yan Han
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  3. Cun-gang Wang
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Correspondence to Jun-qing Li .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. National Dong Hwa University, Shoufeng, Taiwan

    Han-Chieh Chao

  3. China Information Technology Security Evaluation Center, Nanjing, China

    Xingang You

  4. Department of Computer Science, Purdue University, West Lafayette, Indiana, USA

    Elisa Bertino

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Li, Jq., Han, Yy., Wang, Cg. (2017). A Hybrid Artificial Bee Colony Algorithm to Solve Multi-objective Hybrid Flowshop in Cloud Computing Systems. In: Sun, X., Chao, HC., You, X., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2017. Lecture Notes in Computer Science(), vol 10602. Springer, Cham. https://doi.org/10.1007/978-3-319-68505-2_18

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  • DOI: https://doi.org/10.1007/978-3-319-68505-2_18

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