scholarly journals A hybrid bird mating optimizer algorithm with teaching-learning-based optimization for global numerical optimization

10.19139/86 ◽  
2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Qingyang Zhang ◽  
Guolin Yu ◽  
Hui Song
Keyword(s):  
Numerical Optimization ◽  
Global Numerical Optimization ◽  
Teaching Learning Based Optimization ◽  
Bird Mating Optimizer ◽  
Teaching Learning

scholarly journals Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Xiangzhu He ◽  
Jida Huang ◽  
Yunqing Rao ◽  
Liang Gao
Keyword(s):  
Numerical Optimization ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Lévy Flight ◽  
Global Numerical Optimization ◽  
Levy Flight ◽  
Local Optima ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Different Characteristics

Recently, teaching-learning-based optimization (TLBO), as one of the emerging nature-inspired heuristic algorithms, has attracted increasing attention. In order to enhance its convergence rate and prevent it from getting stuck in local optima, a novel metaheuristic has been developed in this paper, where particular characteristics of the chaos mechanism and Lévy flight are introduced to the basic framework of TLBO. The new algorithm is tested on several large-scale nonlinear benchmark functions with different characteristics and compared with other methods. Experimental results show that the proposed algorithm outperforms other algorithms and achieves a satisfactory improvement over TLBO.

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.

TLBO-FLN: Teaching-Learning Based Optimization of Functional Link Neural Networks for Stock Closing Price Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 522-532 ◽  
Author(s):  
Sarat Chandra Nayak ◽  
Subhranginee Das ◽  
Mohammad Dilsad Ansari
Keyword(s):  
Neural Networks ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Fine Tuning ◽  
Functional Link ◽  
Price Prediction ◽  
Closing Price ◽  
Teaching Learning Based Optimization ◽  
Artificial Neural ◽  
Teaching Learning

Background and Objective: Stock closing price prediction is enormously complicated. Artificial Neural Networks (ANN) are excellent approximation algorithms applied to this area. Several nature-inspired evolutionary optimization techniques are proposed and used in the literature to search the optimum parameters of ANN based forecasting models. However, most of them need fine-tuning of several control parameters as well as algorithm specific parameters to achieve optimal performance. Improper tuning of such parameters either leads toward additional computational cost or local optima. Methods: Teaching Learning Based Optimization (TLBO) is a newly proposed algorithm which does not necessitate any parameters specific to it. The intrinsic capability of Functional Link Artificial Neural Network (FLANN) to recognize the multifaceted nonlinear relationship present in the historical stock data made it popular and got wide applications in the stock market prediction. This article presents a hybrid model termed as Teaching Learning Based Optimization of Functional Neural Networks (TLBO-FLN) by combining the advantages of both TLBO and FLANN. Results and Conclusion: The model is evaluated by predicting the short, medium, and long-term closing prices of four emerging stock markets. The performance of the TLBO-FLN model is measured through Mean Absolute Percentage of Error (MAPE), Average Relative Variance (ARV), and coefficient of determination (R2); compared with that of few other state-of-the-art models similarly trained and found superior.

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