scholarly journals Adaptive teaching–learning-based optimization with experience learning to identify photovoltaic cell parameters

2021 ◽  
Vol 7 ◽  
pp. 4114-4125
Author(s):  
Xianyan Mi ◽  
Zuowen Liao ◽  
Shuijia Li ◽  
Qiong Gu
Keyword(s):  
Photovoltaic Cell ◽  
Cell Parameters ◽  
Adaptive Teaching ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

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>

Adaptive Teaching Learning Based Optimization Applied to Nonlinear Economic Load Dispatch Problem

2014 ◽  
Vol 5 (4) ◽  
pp. 1-16 ◽  
Author(s):  
Sk Md Ali Bulbul ◽  
Provas Kumar Roy
Keyword(s):  
Classroom Environment ◽  
Teaching And Learning ◽  
Dynamic Parameter ◽  
Economic Load Dispatch ◽  
System A ◽  
Suitable Parameter ◽  
Adaptive Teaching ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
The Cost

Economic load dispatch (ELD) is a process of calculating real power dispatch by satisfying a set of constraints such a way as fuel cost can be minimized. Inclusion of the effect of valve-points and prohibited operation zones (POZs) in the cost functions make ELD problem a non-linear and non-convex one. For solving ELD in power system a newly proposed evolutionary technique namely adaptive teaching learning based optimization (ATLBO) is presented in this article. TLBO mimics the influence of a teacher on students in a classroom environment by social interaction. ATLBO is an improved version of TLBO which makes TLBO faster and more robust. An adaptive dynamic parameter control mechanism is adopted by the proposed ATLBO algorithm to determine the suitable parameter settings for teaching and learning phases of TLBO algorithm. The proposed ATLBO algorithm is tested in three different cases like 10-unit, 40-unit, and 80-unit systems. A comparison of numerical results with other well established techniques reveals optimization superiority of the proposed scheme both in quality of solution and computational efficiency.

scholarly journals Aircraft Control Parameter Estimation Using Self-Adaptive Teaching-Learning-Based Optimization with an Acceptance Probability

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yodsadej Kanokmedhakul ◽  
Natee Panagant ◽  
Sujin Bureerat ◽  
Nantiwat Pholdee ◽  
Ali R. Yildiz
Keyword(s):  
Parameter Estimation ◽  
Optimization Problem ◽  
Search Performance ◽  
Acceptance Probability ◽  
Inverse Optimization Problem ◽  
Adaptive Teaching ◽  
Teaching Learning Based Optimization ◽  
Aerodynamic Parameters ◽  
Teaching Learning ◽  
Self Adaptive

This work presents a metaheuristic (MH) termed, self-adaptive teaching-learning-based optimization, with an acceptance probability for aircraft parameter estimation. An inverse optimization problem is presented for aircraft longitudinal parameter estimation. The problem is posed to find longitudinal aerodynamic parameters by minimising errors between real flight data and those calculated from the dynamic equations. The HANSA-3 aircraft is used for numerical validation. Several established MHs along with the proposed algorithm are used to solve the proposed optimization problem, while their search performance is investigated compared to a conventional output error method (OEM). The results show that the proposed algorithm is the best performer in terms of search convergence and consistency. This work is said to be the baseline for purely applying MHs for aircraft parameter estimation.

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