Hybrid-TC: A Hybrid Teaching-Learning-Based Optimization Algorithm for Service Composition in Cloud Manufacturing

2021 ◽  
Author(s):  
Juan Yao ◽  
Jun Zeng ◽  
Junhao Wen ◽  
Wei Zhou ◽  
Min Gao
Keyword(s):  
Optimization Algorithm ◽  
Service Composition ◽  
Cloud Manufacturing ◽  
Hybrid Teaching ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

Trust Enforced Cloud Service Composition Based on Teaching–Learning-Based Optimization – STORM

2021 ◽  
pp. 2150293
Author(s):  
V. Meena ◽  
N. Sasikaladevi ◽  
T. Suriya Praba ◽  
V. S. Shankar Sriram
Keyword(s):  
Service Composition ◽  
Optimal Solution ◽  
Cloud Service ◽  
Cloud Manufacturing ◽  
Test Cases ◽  
Trust Value ◽  
Value Maximization ◽  
Teaching Learning Based Optimization ◽  
Cloud Service Composition ◽  
Teaching Learning

In the arena of Cloud Computing, the emergence of social networks and IoT increased the number of available services on the cloud platform, making service composition and optimal selection (SCOS) in Cloud Manufacturing (CMfg), NP-hard. The existing approaches for addressing SCOS often fail to offer assistance with maximized trust and satisfied QoS preferences. Hence, this research paper presents a novel Teach Inglea Rning-based Optimization a Lgorithm (TIROL) for achieving the optimal solution for truST enforced clOud seRvice coMposition (STORM) to assist CMfg for improving the trust value with satisfied QoS preference(s). The performance of the proposed framework has been validated using the synthetic dataset generated from different test-cases. Experimental results show that the proposed framework is reliable and outperforms the SOTA approaches in terms of trust value maximization.

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