Fuzzy Adaptive Teaching Learning-based Optimization Strategy for GUI Functional Test Cases Generation

2018 ◽  
Author(s):  
Fakhrud Din ◽  
Kamal Z. Zamli
Keyword(s):  
Functional Test ◽  
Test Cases ◽  
Optimization Strategy ◽  
Adaptive Teaching ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Fuzzy Adaptive

scholarly journals Short-term optimal hydro-thermal scheduling using clustered adaptive teaching learning based optimization

2019 ◽  
Vol 9 (5) ◽  
pp. 3359
Author(s):  
Surender Reddy Salkuti
Keyword(s):  
Power Generation ◽  
Discharge Rate ◽  
Water Discharge ◽  
Thermal Power ◽  
Test System ◽  
Electric System ◽  
Reservoir Volume ◽  
Adaptive Teaching ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

<span>In this paper, Clustered Adaptive Teaching Learning Based Optimization (CATLBO) algorithm is proposed for determining the optimal hourly schedule of power generation in a hydro-thermal power system. In the proposed approach, a multi-reservoir cascaded hydro-electric system with a non-linear relationship between water discharge rate, net head and power generation is considered. Constraints such as power balance, water balance, reservoir volume limits and operation limits of hydro and thermal plants are considered. The feasibility and effectiveness of the proposed algorithm is demonstrated through a test system, and the results are compared with existing conventional and evolutionary algorithms. Simulation results reveals that the proposed CATLBO algorithm appears to be the best in terms of convergence speed and optimal cost compared with other techniques.</span>

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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