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.

scholarly journals Nonlinear Inertia Weighted Teaching-Learning-Based Optimization for Solving Global Optimization Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Zong-Sheng Wu ◽  
Wei-Ping Fu ◽  
Ru Xue
Keyword(s):  
Global Optimization ◽  
Optimization Problem ◽  
Random Number ◽  
Nonlinear Problems ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Linear And Nonlinear ◽  
Weighted Factor ◽  
Teaching Learning ◽  
Dynamic Inertia

Teaching-learning-based optimization (TLBO) algorithm is proposed in recent years that simulates the teaching-learning phenomenon of a classroom to effectively solve global optimization of multidimensional, linear, and nonlinear problems over continuous spaces. In this paper, an improved teaching-learning-based optimization algorithm is presented, which is called nonlinear inertia weighted teaching-learning-based optimization (NIWTLBO) algorithm. This algorithm introduces a nonlinear inertia weighted factor into the basic TLBO to control the memory rate of learners and uses a dynamic inertia weighted factor to replace the original random number in teacher phase and learner phase. The proposed algorithm is tested on a number of benchmark functions, and its performance comparisons are provided against the basic TLBO and some other well-known optimization algorithms. The experiment results show that the proposed algorithm has a faster convergence rate and better performance than the basic TLBO and some other algorithms as well.

scholarly journals Strengthened Teaching–Learning-Based Optimization Algorithm for Numerical Optimization Tasks

2021 ◽  
Author(s):  
Xuefen Chen ◽  
Chunming Ye ◽  
Yang Zhang ◽  
Lingwei Zhao ◽  
Jing Guo ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Numerical Optimization ◽  
Optimization Techniques ◽  
Data Availability ◽  
Search Performance ◽  
Local Optima ◽  
Cauchy Mutation ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Solution Accuracy

Abstract The teaching–learning-based optimization algorithm (TLBO) is an efficient optimizer. However, it has several shortcomings such as premature convergence and stagnation at local optima. In this paper, the strengthened teaching–learning-based optimization algorithm (STLBO) is proposed to enhance the basic TLBO’s exploration and exploitation properties by introducing three strengthening mechanisms: the linear increasing teaching factor, the elite system composed of new teacher and class leader, and the Cauchy mutation. Subsequently, seven variants of STLBO are designed based on the combined deployment of the three improved mechanisms. Performance of the novel STLBOs is evaluated by implementing them on thirteen numerical optimization tasks. The results show that STLBO7 is at the top of the list, significantly better than the original TLBO. Moreover, the remaining six variants of STLBO also outperform TLBO. Finally, a set of comparisons are implemented between STLBO7 and other advanced optimization techniques. The numerical results and convergence curves prove that STLBO7 clearly outperforms other competitors, has stronger local optimal avoidance, faster convergence speed and higher solution accuracy. All the above manifests that STLBOs has improved the search performance of TLBO. Data Availability Statements: All data generated or analyzed during this study are included in this published article.

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