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

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 chal...

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Veröffentlicht in:Computing Jg. 104; H. 11; S. 2489 - 2509
Hauptverfasser: Jin, Hong, Jiang, Cheng, Lv, Shengping, He, Haiping, Liao, Xinting
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Vienna Springer Vienna 01.11.2022
Springer Nature B.V
Schlagworte:
Adaptive algorithms
Artificial Intelligence
Cloud computing
Composition
Computer Appl. in Administrative Data Processing
Computer Communication Networks
Computer Science
Customer satisfaction
Genetic algorithms
Information Systems Applications (incl.Internet)
Machine learning
Manufacturing
Mutation
Optimization
Quality of service
Regular Paper
Software Engineering
Teaching
Hybrid teaching-learning-based optimization
68T20
Quality of service
90-08
Cloud manufacturing
Service composition
90C27
ISSN:0010-485X, 1436-5057
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0010-485X
1436-5057
DOI:10.1007/s00607-022-01083-4