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A hybrid teaching-learning-based optimization algorithm for QoS-aware manufacturing cloud service composition

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Abstract

Quality of service (QoS)-aware manufacturing cloud service composition (QoS-MCSC) is one of the key issues in Cloud manufacturing (CMfg). More and more manufacturing cloud services offering the same or similar functionality but different QoS attributes are provided in the CMfg platform. It is a challenging issue to construct an optimal composite service satisfying customers’ requirements. In this study, a novel hybrid teaching-learning-based optimization algorithm is proposed to solve QoS-MCSC problems. It integrates the advantages of uniform mutation, adaptive flower pollination algorithm, and teaching-learning-based optimization algorithm. The experimental results show that the proposed algorithm finds higher quality results than other compared algorithms.

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Funding

The work is supported by the Natural Science Foundation of Guangdong Province under Grant Nos. 2018A030310216 and 2021A1515012395, the National Natural Science Foundation of China under Grant No. 51605169.

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Author notes

  1. Cheng Jiang and Shengping Lv have contributed equally to this work.

Authors and Affiliations

  1. College of Engineering, South China Agricultural University, Guangzhou, 510642, China

    Hong Jin, Cheng Jiang, Shengping Lv, Haiping He & Xinting Liao

Authors
  1. Hong Jin
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  2. Cheng Jiang
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  3. Shengping Lv
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  4. Haiping He
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  5. Xinting Liao
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Contributions

H. J. implemented the algorithm and wrote the paper. S. L. edited the paper and improved the quality of the article. C. J., H. H. and X. L. conducted the experiments and analyzed the data. H. J. and S. L. received funding. All authors have read and approved the final manuscript.

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Correspondence to Shengping Lv.

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Jin, H., Jiang, C., Lv, S. et al. A hybrid teaching-learning-based optimization algorithm for QoS-aware manufacturing cloud service composition. Computing 104, 2489–2509 (2022). https://doi.org/10.1007/s00607-022-01083-4

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  • DOI: https://doi.org/10.1007/s00607-022-01083-4

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