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Journal articles on the topic 'Teaching Learning Based Optimization (TLBO)'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

Zou, Feng, Lei Wang, Xinhong Hei, Debao Chen, Qiaoyong Jiang, and Hongye Li. "Bare-Bones Teaching-Learning-Based Optimization." Scientific World Journal 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/136920.

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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 le

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2

Toğan, Vedat, and Ali Mortazavi. "Sizing optimization of skeletal structures using teaching-learning based optimization." An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 7, no. 2 (2017): 130–41. http://dx.doi.org/10.11121/ijocta.01.2017.00309.

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Teaching Learning Based Optimization (TLBO) is one of the non-traditional techniques to simulate natural phenomena into a numerical algorithm. TLBO mimics teaching learning process occurring between a teacher and students in a classroom. A parameter named as teaching factor, TF, seems to be the only tuning parameter in TLBO. Although the value of the teaching factor, TF, is determined by an equation, the value of 1 or 2 has been used by the researchers for TF. This study intends to explore the effect of the variation of teaching factor TF on the performances of TLBO. This effect is demonstrate

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3

Wang, Kai Lin, Hui Bin Wang, Li Xia Yu, Xue Yu Ma, and Yun Sheng Xue. "Teaching-Learning-Based Optimization Algorithm for Dealing with Real-Parameter Optimization Problems." Applied Mechanics and Materials 380-384 (August 2013): 1342–45. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1342.

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A latest optimization algorithm, named Teaching-Learning-Based Optimization (simply TLBO) was proposed by R. V. Rao et al, at 2011. Afterwards, some improvements and practical applications have been conducted toward TLBO algorithm. However, as far as our knowledge, there are no such works which categorize the current works concerning TLBO from the algebraic and analytic points of view. Hence, in this paper we firstly introduce the concepts and algorithms of TLBO, then survey the running mechanism of TLBO for dealing with the real-parameter optimization problems, and finally group its real-worl

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4

Jyoti, Jain. "TLBO – Teaching Learning Based Optimization Algorithm." Advancement of Signal Processing and its Applications 8, no. 1 (2025): 15–21. https://doi.org/10.5281/zenodo.14988251.

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<em>This paper described Teaching Learning Based Optimization Algorithm (TLBO). The objective of this paper is to determine the effect of various parameters, for example, population size, number of iterations, etc. on the result of the algorithm. This paper concluded the population range and number of iterations range for obtaining best solutions with the help of this algorithm.</em>

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Ullah, W., M. A. N. Mu'tasim, and M. F. F. Rashid. "Optimization of Cost-Based Hybrid Flowshop Scheduling Using Teaching-Learning-Based Optimization Algorithm." International Journal of Automotive and Mechanical Engineering 21, no. 3 (2024): 11616–28. http://dx.doi.org/10.15282/ijame.21.3.2024.13.0896.

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A cost-based hybrid flowshop scheduling (CHFS) combines flow shop and job shop elements, with cost considerations as a key indicator. CHFS is a complex combinatorial optimization challenge encountered in real-world manufacturing and production environments. This paper investigates the optimization of a CHFS problem using the Teaching Learning-Based Optimization (TLBO) algorithm. Effective CHFS is crucial for achieving production balance, reducing costs, and improving customer satisfaction. The authors formulate the CHFS scheduling problem and propose applying the TLBO algorithm to minimize tot

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Hubálovský, Štěpán, Marie Hubálovská, and Ivana Matoušová. "A New Hybrid Particle Swarm Optimization–Teaching–Learning-Based Optimization for Solving Optimization Problems." Biomimetics 9, no. 1 (2023): 8. http://dx.doi.org/10.3390/biomimetics9010008.

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This research paper develops a novel hybrid approach, called hybrid particle swarm optimization–teaching–learning-based optimization (hPSO-TLBO), by combining two metaheuristic algorithms to solve optimization problems. The main idea in hPSO-TLBO design is to integrate the exploitation ability of PSO with the exploration ability of TLBO. The meaning of “exploitation capabilities of PSO” is the ability of PSO to manage local search with the aim of obtaining possible better solutions near the obtained solutions and promising areas of the problem-solving space. Also, “exploration abilities of TLB

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7

Wu, Zong-Sheng, Wei-Ping Fu, and Ru Xue. "Nonlinear Inertia Weighted Teaching-Learning-Based Optimization for Solving Global Optimization Problem." Computational Intelligence and Neuroscience 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/292576.

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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 w

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8

Surender, Reddy Salkuti. "Power system state estimation using teaching learning-based optimization algorithm." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 4 (2020): 2125–31. https://doi.org/10.12928/TELKOMNIKA.v18i4.14159.

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The main goal of this paper is to formulate power system state estimation (SE) problem as a constrained nonlinear programming problem with various constraints and boundary limits on the state variables. SE forms the heart of entire real time control of any power system. In real time environment, the state estimator consists of various modules like observability analysis, network topology processing, SE and bad data processing. The SE problem formulated in this work is solved using teaching leaning-based optimization (TLBO) technique. Difference between the proposed TLBO and the conventional op

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Kheireddine, Bourahla, Belli Zoubida, and Hacib Tarik. "Improved version of teaching learning-based optimization algorithm using random local search." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 3 (2019): 1048–60. http://dx.doi.org/10.1108/compel-09-2018-0373.

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Purpose This paper aims to deal with the development of a newly improved version of teaching learning based optimization (TLBO) algorithm. Design/methodology/approach Random local search part was added to the classic optimization process with TLBO. The new version is called TLBO algorithm with random local search (TLBO-RLS). Findings At first step and to validate the effectiveness of the new proposed version of the TLBO algorithm, it was applied to a set of two standard benchmark problems. After, it was used jointly with two-dimensional non-linear finite element method to solve the TEAM worksh

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Wu, Zongsheng, and Ru Xue. "A Cyclical Non-Linear Inertia-Weighted Teaching–Learning-Based Optimization Algorithm." Algorithms 12, no. 5 (2019): 94. http://dx.doi.org/10.3390/a12050094.

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After the teaching–learning-based optimization (TLBO) algorithm was proposed, many improved algorithms have been presented in recent years, which simulate the teaching–learning phenomenon of a classroom to effectively solve global optimization problems. In this paper, a cyclical non-linear inertia-weighted teaching–learning-based optimization (CNIWTLBO) algorithm is presented. This algorithm introduces a cyclical non-linear inertia weighted factor into the basic TLBO to control the memory rate of learners, and uses a non-linear mutation factor to control the learner’s mutation randomly during

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11

Akhilesh, Kumar, and Shahid Mohammad. "Portfolio selection model using teaching learning-based optimization approach." International Journal of Artificial Intelligence (IJ-AI) 12, no. 3 (2023): 1083–90. https://doi.org/10.11591/ijai.v12.i3.pp1083-1090.

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Portfolio selection is among the most challenging processes that have recently increased the interest of professionals in the area. The goal of mean-variance portfolio selection is to maximize expected return with minimizing risk. The Markowitz model was employed to solve the linear portfolio selection problem (PSP). However, due to numerous constraints and complexities, the problem is so critical that traditional models are insufficient to provide efficient solutions. Teaching learning-based optimization (TLBO) is a powerful population-based nature-inspired approach to solve optimization prob

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12

He, Xiangzhu, Jida Huang, Yunqing Rao, and Liang Gao. "Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization." Computational Intelligence and Neuroscience 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/8341275.

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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 p

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Mukherjee, Aparajita, Sourav Paul, and Provas Kumar Roy. "Transient Stability Constrained Optimal Power Flow Using Teaching Learning-Based Optimization." International Journal of Energy Optimization and Engineering 3, no. 4 (2014): 55–71. http://dx.doi.org/10.4018/ijeoe.2014100104.

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Transient stability constrained optimal power flow (TSC-OPF) is a non-linear optimization problem which is not easy to deal directly because of its huge dimension. In order to solve the TSC-OPF problem efficiently, a relatively new optimization technique named teaching learning based optimization (TLBO) is proposed in this paper. TLBO algorithm simulates the teaching–learning phenomenon of a classroom to solve multi-dimensional, linear and nonlinear problems with appreciable efficiency. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solut

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Mukherjee, Aparajita, Sourav Paul, and Provas Kumar Roy. "Transient Stability Constrained Optimal Power Flow Using Teaching Learning Based Optimization." International Journal of Energy Optimization and Engineering 4, no. 1 (2015): 18–35. http://dx.doi.org/10.4018/ijeoe.2015010102.

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Transient stability constrained optimal power flow (TSC-OPF) is a non-linear optimization problem which is not easy to deal directly because of its huge dimension. In order to solve the TSC-OPF problem efficiently, a relatively new optimization technique named teaching learning based optimization (TLBO) is proposed in this paper. TLBO algorithm simulates the teaching–learning phenomenon of a classroom to solve multi-dimensional, linear and nonlinear problems with appreciable efficiency. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solut

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15

Dr., K. Lenin. "LESSENING OF ACTUAL POWER LOSS BY MODIFIED ALGORITHM." International Journal of Research - Granthaalayah 6, no. 8 (2018): 159–67. https://doi.org/10.5281/zenodo.1403848.

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This paper presents a Modified Teaching-Learning-Based Optimization (MTLBO) algorithm for solving reactive power flow problem. Basic Teaching-Learning-Based Optimization (TLBO) is reliable, accurate and vigorous for solving the optimization problems. Also, it has been found that TLBO algorithm slow in convergence due to its high concentration in the accuracy. This paper presents an, Modified version of TLBO algorithm, called as Modified Teaching-Learning-Based Optimization (MTLBO). A parameter called as “weight” has been included in the fundamental TLBO equations & subsequently

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16

Dib, Nihad I. "Synthesis of thinned planar antenna arrays using teaching–learning-based optimization." International Journal of Microwave and Wireless Technologies 7, no. 5 (2014): 557–63. http://dx.doi.org/10.1017/s1759078714000798.

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In this paper, the design of thinned planar antenna arrays of isotropic radiators with optimum side lobe level reduction is studied. The teaching–learning-based optimization (TLBO) method, a newly proposed global evolutionary optimization method, is used to determine an optimum set of turned-ON elements of thinned planar antenna arrays that provides a radiation pattern with optimum side lobe level reduction. The TLBO represents a new algorithm for optimization problems in antenna arrays design. It is shown that the TLBO provides results that are better than (or the same as) those obtained usin

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Kumar, Akhilesh, and Mohammad Shahid. "Portfolio selection model using teaching learning-based optimization approach." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 3 (2023): 1083. http://dx.doi.org/10.11591/ijai.v12.i3.pp1083-1090.

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<p>Portfolio selection is among the most challenging processes that have recently increased the interest of professionals in the area. The goal of mean-variance portfolio selection is to maximize expected return with minimizing risk. The Markowitz model was employed to solve the linear portfolio selection problem. However, due to numerous constraints and complexities, the problem is so critical that traditional models are insufficient to provide efficient solutions. Teaching learning-based optimization (TLBO) is a powerful population-based nature-inspired approach to solve optimization p

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18

Lenin, K. "Minimization of real power loss by enhanced teaching learning based optimization algorithm." IAES International Journal of Robotics and Automation (IJRA) 9, no. 1 (2020): 1. http://dx.doi.org/10.11591/ijra.v9i1.pp1-5.

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<p class="Abstract">This paper presents an Enhanced Teaching-Learning-Based Optimization (ETLBO) algorithm for solving reactive power flow problem. Basic Teaching-Learning-Based Optimization (TLBO) is reliable, accurate and vigorous for solving the optimization problems. Also it has been found that TLBO algorithm slow in convergence due to its high concentration in the accuracy. This paper presents an, enhanced version of TLBO algorithm, called as enhanced Teaching-Learning-Based Optimization (ETLBO). A parameter called as “weight” has been included in the fundamental TLBO equations &amp

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Kanagasabai, Lenin. "Advanced Teaching-Learning-Based Optimization Algorithm for Actual Power Loss Reduction." IAES International Journal of Robotics and Automation (IJRA) 9, no. 1 (2019): 46. http://dx.doi.org/10.11591/ijra.v9i1.pp46-50.

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In this work Advanced Teaching-Learning-Based Optimization algorithm (ATLBO) is proposed to solve the optimal reactive power problem. Teaching-Learning-Based Optimization (TLBO) optimization algorithm has been framed on teaching learning methodology happening in classroom. Algorithm consists of “Teacher Phase”, “Learner Phase”. In the proposed Advanced Teaching-Learning-Based Optimization algorithm non-linear inertia weighted factor is introduced into the fundamental TLBO algorithm to manage the memory rate of learners. In order to control the learner’s mutation arbitrarily during the learning

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Lenin, K. "LESSENING OF ACTUAL POWER LOSS BY MODIFIED ALGORITHM." International Journal of Research -GRANTHAALAYAH 6, no. 8 (2018): 159–67. http://dx.doi.org/10.29121/granthaalayah.v6.i8.2018.1418.

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This paper presents a Modified Teaching-Learning-Based Optimization (MTLBO) algorithm for solving reactive power flow problem. Basic Teaching-Learning-Based Optimization (TLBO) is reliable, accurate and vigorous for solving the optimization problems. Also, it has been found that TLBO algorithm slow in convergence due to its high concentration in the accuracy. This paper presents an, Modified version of TLBO algorithm, called as Modified Teaching-Learning-Based Optimization (MTLBO). A parameter called as “weight” has been included in the fundamental TLBO equations & subsequently it increase

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21

Ma, Yindi, Yanhai Li, and Longquan Yong. "Teaching–Learning-Based Optimization Algorithm with Stochastic Crossover Self-Learning and Blended Learning Model and Its Application." Mathematics 12, no. 10 (2024): 1596. http://dx.doi.org/10.3390/math12101596.

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This paper presents a novel variant of the teaching–learning-based optimization algorithm, termed BLTLBO, which draws inspiration from the blended learning model, specifically designed to tackle high-dimensional multimodal complex optimization problems. Firstly, the perturbation conditions in the “teaching” and “learning” stages of the original TLBO algorithm are interpreted geometrically, based on which the search capability of the TLBO is enhanced by adjusting the range of values of random numbers. Second, a strategic restructuring has been ingeniously implemented, dividing the algorithm int

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Chen, Xu, Bin Xu, Kunjie Yu, and Wenli Du. "Teaching-Learning-Based Optimization with Learning Enthusiasm Mechanism and Its Application in Chemical Engineering." Journal of Applied Mathematics 2018 (2018): 1–19. http://dx.doi.org/10.1155/2018/1806947.

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Teaching-learning-based optimization (TLBO) is a population-based metaheuristic search algorithm inspired by the teaching and learning process in a classroom. It has been successfully applied to many scientific and engineering applications in the past few years. In the basic TLBO and most of its variants, all the learners have the same probability of getting knowledge from others. However, in the real world, learners are different, and each learner’s learning enthusiasm is not the same, resulting in different probabilities of acquiring knowledge. Motivated by this phenomenon, this study introd

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Zou, Feng, Lei Wang, Debao Chen, and Xinhong Hei. "An Improved Teaching-Learning-Based Optimization with Differential Learning and Its Application." Mathematical Problems in Engineering 2015 (2015): 1–19. http://dx.doi.org/10.1155/2015/754562.

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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 op

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Liu, Peijun. "Intelligent optimization algorithm-driven informational teaching model for English reading and writing in universities." Journal of Computational Methods in Sciences and Engineering 24, no. 2 (2024): 715–30. http://dx.doi.org/10.3233/jcm-237101.

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English reading and writing are important parts of language teaching. In order to improve the English reading and writing ability of college students, the TLBO (teaching learning-based optimization) algorithm is used in this research to improve the way that English reading and writing are taught in colleges and universities. It is chosen as the primary model for this study. The TLBO algorithm is further optimized in this paper, and a convergence analysis is performed between the optimized model M-TLBO (multi-learning teaching learning-based optimization) algorithm and other TLBO algorithms in

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Lenin, Kanagasabai. "Advanced teaching-learning-based optimization algorithm for actual power loss reduction." IAES International Journal of Robotics and Automation (IJRA) 9, no. 1 (2020): 46–50. https://doi.org/10.11591/ijra.v9i1.pp46-50.

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In this work Advanced Teaching-Learning-Based Optimization algorithm (ATLBO) is proposed to solve the optimal reactive power problem. Teaching-Learning-Based Optimization (TLBO) optimization algorithm has been framed on teaching learning methodology happening in classroom. Algorithm consists of “Teacher Phase”, “Learner Phase”. In the proposed Advanced Teaching-Learning-Based Optimization algorithm non-linear inertia weighted factor is introduced into the fundamental TLBO algorithm to manage the memory rate of learners. In order to con

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Zhai, Zhibo, Yusen Dai, and Yingfang Xue. "A Novel Teaching-Learning-Based Optimization with Laplace Distribution and Experience Exchange." Mathematical Problems in Engineering 2022 (April 26, 2022): 1–13. http://dx.doi.org/10.1155/2022/4177405.

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Teaching-Learning-Based Optimization (TLBO) algorithm is an evolutionary powerful algorithm that has better global searching capability. However, in the later period of evolution of the TLBO algorithm, the diversity of learners will be degraded with the increasing iteration of evolution and the smaller scope of solutions, which lead to a trap in local optima and premature convergence. This paper presents an improved version of the TLBO algorithm based on Laplace distribution and Experience exchange strategy (LETLBO). It uses Laplace distribution to expand exploration space. A new experience ex

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Zou, Feng, Debao Chen, and Jiangtao Wang. "An Improved Teaching-Learning-Based Optimization with the Social Character of PSO for Global Optimization." Computational Intelligence and Neuroscience 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/4561507.

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An improved teaching-learning-based optimization with combining of the social character of PSO (TLBO-PSO), which is considering the teacher’s behavior influence on the students and the mean grade of the class, is proposed in the paper to find the global solutions of function optimization problems. In this method, the teacher phase of TLBO is modified; the new position of the individual is determined by the old position, the mean position, and the best position of current generation. The method overcomes disadvantage that the evolution of the original TLBO might stop when the mean position of s

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Mahdie, Khorashadizade, Jouyban Morteza, and Asghari Oskoei Mohammadreza. "A hybrid constructive algorithm incorporating teaching-learning based optimization for neural network training." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 3425–733. https://doi.org/10.11591/ijece.v10i4.pp3425-3733.

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In neural networks, simultaneous determination of the optimum structure and weights is a challenge. This paper proposes a combination of teaching-learning based optimization (TLBO) algorithm and a constructive algorithm (CA) to cope with the challenge. In literature, TLBO is used to choose proper weights, while CA is adopted to construct different structures in order to select the proper one. In this study, the basic TLBO algorithm along with an improved version of this algorithm for network weights selection are utilized. Meanwhile, as a constructive algorithm, a novel modification to multipl

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Bulbul, Sk Md Ali, and Provas Kumar Roy. "Adaptive Teaching Learning Based Optimization Applied to Nonlinear Economic Load Dispatch Problem." International Journal of Swarm Intelligence Research 5, no. 4 (2014): 1–16. http://dx.doi.org/10.4018/ijsir.2014100101.

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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

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Chaudhary, Kailash, and Himanshu Chaudhary. "Optimal dynamic design of planar mechanisms using teaching–learning-based optimization algorithm." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 230, no. 19 (2016): 3442–56. http://dx.doi.org/10.1177/0954406215612831.

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A two-stage optimization method for optimal dynamic design of planar mechanisms is presented in this paper. For dynamic balancing, minimization of the shaking force and the shaking moment is achieved by finding optimum mass distribution of mechanism links using the equimomental system of point-masses in the first stage of the optimization. In the second stage, their shapes are synthesized systematically by closed parametric curve, i.e. cubic B-spline curve corresponding to the optimum inertial parameters found in the first stage. The multi-objective optimization problem to minimize both the sh

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Chen, Yan-Kwang, Shi-Xin Weng, and Tsai-Pei Liu. "Teaching–Learning Based Optimization (TLBO) with Variable Neighborhood Search to Retail Shelf-Space Allocation." Mathematics 8, no. 8 (2020): 1296. http://dx.doi.org/10.3390/math8081296.

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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

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Kaur, Amanpreet, Heena Wadhwa, Pardeep Singh, and Harpreet Kaur Toor. "Teaching Learning Based Optimization (TLBO) for energy efficiency in Fog Computing." CGC International Journal of Contemporary Technology and Research 4, no. 1 (2021): 248–52. http://dx.doi.org/10.46860/cgcijctr.2021.12.31.248.

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Fog Computing is eminent to ensure quality of service in handling huge volume and variety of data and to display output, or for closed loop process control. It comprises of fog devices to manage huge data transmission but results in high energy consumption, end-to end-delay, latency. In this paper, an energy model for fog computing environment has been proposed and implemented based on teacher student learning model called Teaching Learning Based Optimization (TLBO) to improve the responsiveness of the fog network in terms of energy optimization. The results show the effectiveness of TLBO in c

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Roy, Adhit, Susanta Dutta, and Provas Kumar Roy. "Load Frequency Control of Interconnected Power System Using Teaching Learning Based Optimization." International Journal of Energy Optimization and Engineering 4, no. 1 (2015): 102–17. http://dx.doi.org/10.4018/ijeoe.2015010107.

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This paper presents the design and performance analysis of teaching learning based optimization (TLBO) algorithm based PID controller for load frequency control (LFC) of an interconnected power system. A two area reheat thermal system equipped with PID controllers which is widely used in literature is considered for the design and analysis purpose. The design objective is to improve the transient performance of the interconnected system. The power system dynamic performance is analyzed based on time response plots achieved with the implementation of designed optimal and sub-optimal LFC regulat

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Gu, Fahui, Wenxiang Wang, and Luyan Lai. "Improved Teaching-Learning-Based Optimization Algorithm and its Application in PID Parameter Optimization." International Journal of Cognitive Informatics and Natural Intelligence 13, no. 2 (2019): 1–17. http://dx.doi.org/10.4018/ijcini.2019040101.

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The teaching-learning-based optimization (TLBO) algorithm has been applied to many optimization problems, but its theoretical basis is relatively weak, its control parameters are difficult to choose, and it converges slowly in the late period and makes it too early to mature. To overcome these shortcomings, this article proposes a dual-population co-evolution teaching and learning optimization algorithm (DPCETLBO) in which adaptive learning factors and a multi-parent non-convex hybrid elite strategy are introduced for a population with high fitness values to improve the convergence speed of th

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Khorashadizade, Mahdie, Morteza Jouyban, and Mohammadreza Asghari Oskoei. "A hybrid constructive algorithm incorporating teaching-learning based optimization for neural network training." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 3725. http://dx.doi.org/10.11591/ijece.v10i4.pp3725-3733.

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In neural networks, simultaneous determination of the optimum structure and weights is a challenge. This paper proposes a combination of teaching-learning based optimization (TLBO) algorithm and a constructive algorithm (CA) to cope with the challenge. In literature, TLBO is used to choose proper weights, while CA is adopted to construct different structures in order to select the proper one. In this study, the basic TLBO algorithm along with an improved version of this algorithm for network weights selection are utilized. Meanwhile, as a constructive algorithm, a novel modification to multipl

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Wu, Di, Shuang Wang, Qingxin Liu, Laith Abualigah, and Heming Jia. "An Improved Teaching-Learning-Based Optimization Algorithm with Reinforcement Learning Strategy for Solving Optimization Problems." Computational Intelligence and Neuroscience 2022 (March 24, 2022): 1–24. http://dx.doi.org/10.1155/2022/1535957.

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This paper presents an improved teaching-learning-based optimization (TLBO) algorithm for solving optimization problems, called RLTLBO. First, a new learning mode considering the effect of the teacher is presented. Second, the Q-Learning method in reinforcement learning (RL) is introduced to build a switching mechanism between two different learning modes in the learner phase. Finally, ROBL is adopted after both the teacher and learner phases to improve the local optima avoidance ability of RLTLBO. These two strategies effectively enhance the convergence speed and accuracy of the proposed algo

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Thamaraikannan, B., and V. Thirunavukkarasu. "Design Optimization of Mechanical Components Using an Enhanced Teaching-Learning Based Optimization Algorithm with Differential Operator." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/309327.

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This paper studies in detail the background and implementation of a teaching-learning based optimization (TLBO) algorithm with differential operator for optimization task of a few mechanical components, which are essential for most of the mechanical engineering applications. Like most of the other heuristic techniques, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. A differential operator is incorporated into the TLBO for effective search of better solutions. To validate the effectiveness of the proposed method, three typical optimi

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Mukhopadhyay, Pranabesh, Susanta Dutta, and Provas Kumar Roy. "Optimal Location of TCSC Using Opposition Teaching Learning Based Optimization." International Journal of Energy Optimization and Engineering 4, no. 1 (2015): 85–101. http://dx.doi.org/10.4018/ijeoe.2015010106.

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This paper focuses on the optimal power flow solution and the enhancement of the performance of a power system network. The paper presents a secured optimal power flow solution by integrating Thyristor controlled series compensator (TCSC) with the optimization model developed under overload condition. The Teaching Learning Based Optimization (TLBO) has been implemented here. Recently, the opposition-based learning (OBL) technique has been applied in various conventional population based techniques to improve the convergence performance and get better simulation results. In this paper, oppositi

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Zhai, Zhibo, Shujuan Li, and Yong Liu. "Parameter Determination of Milling Process Using a Novel Teaching-Learning-Based Optimization Algorithm." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/425689.

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Cutting parameter optimization dramatically affects the production time, cost, profit rate, and the quality of the final products, in milling operations. Aiming to select the optimum machining parameters in multitool milling operations such as corner milling, face milling, pocket milling, and slot milling, this paper presents a novel version of TLBO, TLBO with dynamic assignment learning strategy (DATLBO), in which all the learners are divided into three categories based on their results in “Learner Phase”: good learners, moderate learners, and poor ones. Good learners are self-motivated and t

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S, Anbazhagan. "APPLICATION OF TEACHING LEARNING BASED OPTIMIZATION IN MULTILEVEL IMAGE THRESHOLDING." ICTACT Journal on Image and Video Processing 11, no. 4 (2021): 2413–22. http://dx.doi.org/10.21917/ijivp.2021.0344.

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This paper proposes a Teaching learning-based optimization (TLBO) algorithm for the multilevel image thresholding using Kapur entropy. In image processing, the thresholding arises to help medical imaging, detection, and recognition in making an informed decision about the image. However, they are computationally expensive reaching out to multilevel thresholding since they thoroughly search the optimal thresholds to enhance the fitness functions. In order to validate the chaotic characteristic of multilevel thresholding, a TLBO algorithm is modeled. The proposed model is an algorithm-specific,

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Zhai, Zhibo, Guoping Jia, and Kai Wang. "A Novel Teaching-Learning-Based Optimization with Error Correction and Cauchy Distribution for Path Planning of Unmanned Air Vehicle." Computational Intelligence and Neuroscience 2018 (August 1, 2018): 1–12. http://dx.doi.org/10.1155/2018/5671709.

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Teaching-learning-based optimization (TLBO) algorithm is a novel heuristic method which simulates the teaching-learning phenomenon of a classroom. However, in the later period of evolution of the TLBO algorithm, the lower exploitation ability and the smaller scope of solutions led to the poor results. To address this issue, this paper proposes a novel version of TLBO that is augmented with error correction strategy and Cauchy distribution (ECTLBO) in which Cauchy distribution is utilized to expand the searching space and error correction to avoid detours to achieve more accurate solutions. The

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Hind, Raad Ibraheem, Faiz Hussain Zahraa, Mazin Ali Sura, Aljanabi Mohammad, Abdulghafoor Mohammed Mostafa, and Sutikno Tole. "A new model for large dataset dimensionality reduction based on teaching learning-based optimization and logistic regression." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 3 (2020): 1688–94. https://doi.org/10.12928/TELKOMNIKA.v18i3.13764.

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One of the human diseases with a high rate of mortality each year is breast cancer (BC). Among all the forms of cancer, BC is the commonest cause of death among women globally. Some of the effective ways of data classification are data mining and classification methods. These methods are particularly efficient in the medical field due to the presence of irrelevant and redundant attributes in medical datasets. Such redundant attributes are not needed to obtain an accurate estimation of disease diagnosis. Teaching learning-based optimization (TLBO) is a new metaheuristic that has been successful

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Cano Ortega, Antonio, Francisco Jose Sánchez Sutil, and Jesús De la Casa Hernández. "Power Factor Compensation Using Teaching Learning Based Optimization and Monitoring System by Cloud Data Logger." Sensors 19, no. 9 (2019): 2172. http://dx.doi.org/10.3390/s19092172.

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The main objective of this paper is to compensate power factor using teaching learning based optimization (TLBO), determine the capacitor bank optimization (CBO) algorithm, and monitor a system in real-time using cloud data logging (CDL). Implemented Power Factor Compensation and Monitoring System (PFCMS) calculates the optimal capacitor combination to improve power factor of the installation by measure of voltage, current, and active power. CBO algorithm determines the best solution of capacitor values to install, by applying TLBO in different phases of the algorithm. Electrical variables acq

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Wu, Di, Heming Jia, Laith Abualigah, et al. "Enhance Teaching-Learning-Based Optimization for Tsallis-Entropy-Based Feature Selection Classification Approach." Processes 10, no. 2 (2022): 360. http://dx.doi.org/10.3390/pr10020360.

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Feature selection is an effective method to reduce the number of data features, which boosts classification performance in machine learning. This paper uses the Tsallis-entropy-based feature selection to detect the significant feature. Support Vector Machine (SVM) is adopted as the classifier for classification purposes in this paper. We proposed an enhanced Teaching-Learning-Based Optimization (ETLBO) to optimize the SVM and Tsallis entropy parameters to improve classification accuracy. The adaptive weight strategy and Kent chaotic map are used to enhance the optimal ability of the traditiona

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Subhakanta, Bal, Swain Srinibash, and Sarathi Khuntia Partha. "Bio inspired technique for controlling angle of attack of aircraft." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4206–16. https://doi.org/10.11591/ijai.v13.i4.pp4206-4216.

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This paper deals with the design of a proportional–integral (PI) controller for controlling the angle of attack of flight control system. For the first time teaching learning-based optimization (TLBO) algorithm is applied in this area to obtain the parameters of the proposed PI controller. The design problem is formulated as an optimization problem and TLBO is employed to optimize the parameters of the PI controller. The superiority of proposed approach is demonstrated by comparing the results with that of the conventional methods like genetic algorithm (GA) and particle swarm optimizati

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Zhang, Peipeng, and Fangfang Zhang. "Enhancing Education and Teaching Management Through Data Mining and Support Vector Machine Algorithm." International Journal of e-Collaboration 20, no. 1 (2024): 1–16. http://dx.doi.org/10.4018/ijec.357998.

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This study investigates the effectiveness of the SVM+TLBO approach for predicting student performance and evaluating learning abilities in educational settings. By integrating Support Vector Machines (SVM) with Teaching-Learning-Based Optimization (TLBO), the research aims to enhance predictive accuracy and efficiency compared to traditional methods, including Decision Trees, Ant Colony Algorithms, Clustering Algorithms, Convolutional Neural Networks (CNN), Neural Networks, Support Vector Machine (SVM) without Optimization. Results indicate that the SVM+TLBO model significantly outperforms the

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Katal, Nitish, and Shiv Narayan. "QFT Based Robust Positioning Control of the PMSM Using Automatic Loop Shaping with Teaching Learning Optimization." Modelling and Simulation in Engineering 2016 (2016): 1–18. http://dx.doi.org/10.1155/2016/9837058.

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Automation of the robust control system synthesis for uncertain systems is of great practical interest. In this paper, the loop shaping step for synthesizing quantitative feedback theory (QFT) based controller for a two-phase permanent magnet stepper motor (PMSM) has been automated using teaching learning-based optimization (TLBO) algorithm. The QFT controller design problem has been posed as an optimization problem and TLBO algorithm has been used to minimize the proposed cost function. This facilitates designing low-order fixed-structure controller, eliminates the need of manual loop shaping

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Kumar, Vidyapati, Sunny Diyaley, and Shankar Chakraborty. "TEACHING-LEARNING-BASED PARAMETRIC OPTIMIZATION OF AN ELECTRICAL DISCHARGE MACHINING PROCESS." Facta Universitatis, Series: Mechanical Engineering 18, no. 2 (2020): 281. http://dx.doi.org/10.22190/fume200218028k.

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Due to several unique features, electrical discharge machining (EDM) has proved itself as one of the efficient non-traditional machining processes for generating intricate shape geometries on various advanced engineering materials in order to fulfill the requirement of the present day manufacturing industries. In this paper, the machining capability of an EDM process is studied during standard hole making operation on pearlitic SG iron 450/12 grade material, while considering gap voltage, peak current, cycle time and tool rotation as input parameters. On the other hand, material removal rate,

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López-Martínez, Alan, and Francisco Javier Cuevas. "Multiple View Relations Using the Teaching and Learning-Based Optimization Algorithm." Computers 9, no. 4 (2020): 101. http://dx.doi.org/10.3390/computers9040101.

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In computer vision, estimating geometric relations between two different views of the same scene has great importance due to its applications in 3D reconstruction, object recognition and digitization, image registration, pose retrieval, visual tracking and more. The Random Sample Consensus (RANSAC) is the most popular heuristic technique to tackle this problem. However, RANSAC-like algorithms present a drawback regarding either the tuning of the number of samples and the threshold error or the computational burden. To relief this problem, we propose an estimator based on a metaheuristic, the T

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Elshaboury, Nehal, Eslam Mohammed Abdelkader, Abobakr Al-Sakkaf, and Ghasan Alfalah. "Teaching-Learning-Based Optimization of Neural Networks for Water Supply Pipe Condition Prediction." Water 13, no. 24 (2021): 3546. http://dx.doi.org/10.3390/w13243546.

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The bulk of water pipes experience major degradation and deterioration problems. This research aims at estimating the condition of water pipes in Shattora and Shaker Al-Bahery’s water distribution networks, in Egypt. The developed models involve training the Elman neural network (ENN) and feed-forward neural network (FFNN) coupled with particle swarm optimization (PSO), genetic algorithms (GA), the sine cosine algorithm (SCA), and the teaching-learning-based optimization (TLBO) algorithm. For the Shattora network, the inputs to these models are pipe characteristics such as length, wall thickne

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