An improved teaching–learning-based optimization with neighborhood search for applications of ANN

Teaching-learning-based optimization (TLBO) algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.

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An Improved Teaching-Learning-Based Optimization with Differential Learning and Its Application

Mathematical Problems in Engineering ◽

10.1155/2015/754562 ◽

2015 ◽

Vol 2015 ◽

pp. 1-19 ◽

Cited By ~ 4

Author(s):

Feng Zou ◽

Lei Wang ◽

Debao Chen ◽

Xinhong Hei

Keyword(s):

Learning Strategy ◽

Population Based ◽

Neighborhood Search ◽

New Mutation ◽

Iir Filter ◽

Differential Learning ◽

Tlbo Algorithm ◽

Teaching Learning Based Optimization ◽

Digital Iir Filter ◽

Teaching Learning

The teaching-learning-based optimization (TLBO) algorithm is a population-based optimization algorithm which is based on the effect of the influence of a teacher on the output of learners in a class. A variant of teaching-learning-based optimization (TLBO) algorithm with differential learning (DLTLBO) is proposed in the paper. In this method, DLTLBO utilizes a learning strategy based on neighborhood search of teacher phase in the standard TLBO to generate a new mutation vector, while utilizing a differential learning to generate another new mutation vector. Then DLTLBO employs the crossover operation to generate new solutions so as to increase the diversity of the population. By the integration of the local search and the global search, DLTLBO achieves a tradeoff between exploration and exploitation. To demonstrate the effectiveness of our approaches, 24 benchmark functions are used for simulating and testing. Moreover, DLTLBO is used for parameter estimation of digital IIR filter and experimental results show that DLTLBO is superior or comparable to other given algorithms for the employed examples.

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Teaching–Learning Based Optimization (TLBO) with Variable Neighborhood Search to Retail Shelf-Space Allocation

Mathematics ◽

10.3390/math8081296 ◽

2020 ◽

Vol 8 (8) ◽

pp. 1296

Author(s):

Yan-Kwang Chen ◽

Shi-Xin Weng ◽

Tsai-Pei Liu

Keyword(s):

Variable Neighborhood Search ◽

Neighborhood Search ◽

Shelf Space ◽

The Past ◽

Space Allocation ◽

Solution Methods ◽

Teaching Learning Based Optimization ◽

Shelf Space Allocation ◽

Teaching Learning ◽

Product Sales

Shelf space is a scarce and expensive resource in the retail industry because a large number of products compete for limited display space. Thus, shelf-space allocation is frequently implemented in shops to increase product sales and profits. In the past few decades, numerous models and solution methods have been developed to deal with the shelf-space allocation problem (SSAP). In this paper, a novel population-oriented metaheuristic algorithm, teaching–learning-based optimization (TLBO) is applied to solve the problem and compared with existing solution methods with respect to their solution performance. Further, a hybrid algorithm that combines TLBO with variable neighborhood search (VNS) is proposed to enhance the performance of the basic TLBO. The research results show that the proposed TLBO-VNS algorithm is superior to other algorithms in terms of solution performance, in addition to using fewer control parameters. Therefore, the proposed TLBO-VNS algorithm has considerable potential in solving SSAP.

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Optimal trajectory planning for robotic manipulators using improved teaching-learning-based optimization algorithm

Industrial Robot the international journal of robotics research and application ◽

10.1108/ir-08-2015-0167 ◽

2016 ◽

Vol 43 (3) ◽

pp. 308-316 ◽

Cited By ~ 14

Author(s):

Xueshan Gao ◽

Yu Mu ◽

Yongzhuo Gao

Keyword(s):

Trajectory Planning ◽

Optimal Trajectory ◽

Robotic Manipulators ◽

Neighborhood Search ◽

Local Optimum ◽

Content Type ◽

Time Optimal ◽

Teaching Learning Based Optimization ◽

Teaching Learning ◽

Optimal Trajectory Planning

Purpose The purpose of this paper is to propose a method of optimal trajectory planning for robotic manipulators that applies an improved teaching-learning-based optimization (ITLBO) algorithm. Design/methodology/approach The ITLBO algorithm possesses better ability to escape from the local optimum by integrating the original TLBO with variable neighborhood search. The trajectory of robotic manipulators complying with the kinematical constraints is constructed by fifth-order B-spline curves. The objective function to be minimized is execution time of the trajectory. Findings Experimental results with a 6-DOF robotic manipulator applied to surface polishing of metallic workpiece verify the effectiveness of the method. Originality/value The presented ITLBO algorithm is more efficient than the original TLBO algorithm and its variants. It can be applied to any robotic manipulators to generate time-optimal trajectories.

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Optimal Distributed Generation Placement for Maximum Loss Reduction using Teaching Learning Based Optimization through Matlab GUI

i-manager s Journal on Power Systems Engineering ◽

10.26634/jps.2.4.3112 ◽

2015 ◽

Vol 2 (4) ◽

pp. 21-28 ◽

Cited By ~ 2

Author(s):

M. Harini ◽

◽

M. Damodar Reddy ◽

Keyword(s):

Distributed Generation ◽

Loss Reduction ◽

Maximum Loss ◽

Teaching Learning Based Optimization ◽

Teaching Learning ◽

Distributed Generation Placement

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Voltage Stability Improvement and Loss Minimization by Optimal Placement of STATCOM using Teaching- Learning Based Optimization Technique

SSRN Electronic Journal ◽

10.2139/ssrn.3575346 ◽

2020 ◽

Author(s):

Manish Kumar Meena ◽

Yogendra Kumar ◽

Rishi Kumar ◽

Amit Kumar

Keyword(s):

Voltage Stability ◽

Optimization Technique ◽

Optimal Placement ◽

Loss Minimization ◽

Teaching Learning Based Optimization ◽

Teaching Learning

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

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327909666191202113015 ◽

2020 ◽

Vol 10 (4) ◽

pp. 522-532 ◽

Cited By ~ 1

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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