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A two-phase method to optimize service composition in cloud manufacturing

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Abstract

Service composition is widely employed in cloud manufacturing. Due to the abundance of similar cloud manufacturing services, the search space for optimizing service composition tends to be expansive. Existing optimization models primarily focus on QoS (quality of service) while often neglecting QoC (quality of collaboration). Furthermore, there remains scope for improving the quality and stability of service composition optimization. Therefore, this paper proposes a two-phase method for optimizing service composition in cloud manufacturing. In the first phase, we introduce a service cluster-oriented service response framework, efficiently generating the candidate response service set to reduce solution search space. In the second phase, we construct an optimization model that integrates QoS and QoC. Subsequently, we devise an artificial bee colony (ABC) algorithm incorporating a multi-search strategy island model to optimize cloud manufacturing service composition. Experimental results demonstrate that the introduction of service clusters enhances search efficiency, with the proposed method outperforming compared ABC algorithms and other swarm intelligence algorithms in optimization quality and stability.

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The data used to support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

The authors would like to thank to Yuqing Tian for her invaluable suggestions on enhancing the artificial bee colony algorithm.

Funding

This work is supported by the Natural science foundation of China under Grant 61973180, and the Key Research Program of Shandong Province (Soft Sciences) under grant 2023RKY01009, and the Foundation of Yunnan Key Laboratory of Service Computing under Grant YNSC23116.

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Authors and Affiliations

  1. College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China

    Qiang Hu, Haoquan Qi, Yanzhe Jia & Lianen Qu

  2. Yunnan Key Laboratory of Service Computing, Kunming, 650214, China

    Qiang Hu

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  1. Qiang Hu
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  2. Haoquan Qi
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  3. Yanzhe Jia
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  4. Lianen Qu
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Contributions

Qiang HU designed the optimization model and wrote the main manuscript text. Haoquan Qi improved the artificial bee colony algorithm. Yanzhe Jia and Lianen Qu designed and carried out experimental verification.

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Correspondence to Lianen Qu.

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Hu, Q., Qi, H., Jia, Y. et al. A two-phase method to optimize service composition in cloud manufacturing. Computing (2024). https://doi.org/10.1007/s00607-024-01286-x

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