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A hybrid teaching-learning-based optimization algorithm for QoS-aware manufacturing cloud service composition
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DOI：10.1007/s00607-022-01083-4.png)
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摘要
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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.
Keyword:
Cloud manufacturing
Service composition
Quality of service
Hybrid teaching-learning-based optimization
期刊
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2.8
论文数:
2.3K
被引数:
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