CTLBO: Converged teaching–learning–based optimization

Teaching–learning–based optimization (TLBO) is an algorithm based on the influence of a teacher on the output of learners in a class. This method has shown to be more effective and efficient than other optimizations in finding the maximum solutions. In this paper, a new improved version of TLBO algo...

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Autores principales:	M. J. Mahmoodabadi, R. Ostadzadeh
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2019
Materias:	teaching–learning–based optimization convergence operator benchmark problems humanoid robot fuzzy control Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/379f0f639f0d47a4a1084b78ed0faee0
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id	oai:doaj.org-article:379f0f639f0d47a4a1084b78ed0faee0
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spelling	oai:doaj.org-article:379f0f639f0d47a4a1084b78ed0faee02021-11-04T15:51:56ZCTLBO: Converged teaching–learning–based optimization2331-191610.1080/23311916.2019.1654207https://doaj.org/article/379f0f639f0d47a4a1084b78ed0faee02019-01-01T00:00:00Zhttp://dx.doi.org/10.1080/23311916.2019.1654207https://doaj.org/toc/2331-1916Teaching–learning–based optimization (TLBO) is an algorithm based on the influence of a teacher on the output of learners in a class. This method has shown to be more effective and efficient than other optimizations in finding the maximum solutions. In this paper, a new improved version of TLBO algorithm, called the converged teaching-learning-based optimization (CTLBO), is presented. In fact, it combines a proposed convergence operator with the teacher phase to find better solutions with a higher convergence rate. The method is tested on some benchmark problems and the results are compared with the original TLBO and other popular evolutionary algorithms. Furthermore, the introduced algorithm is used for optimization of fuzzy tracking control of a walking humanoid robot. In elaboration, fuzzy tracking control, which has appropriate membership functions and error indices, is employed in this paper as a promising intelligent approach to control the nonlinear dynamics of a humanoid robot. Summation of integrals of absolute angle errors and absolute control efforts is regarded as the objective function addressed by both TLBO and CTLBO algorithms in the present investigation.M. J. MahmoodabadiR. OstadzadehTaylor & Francis Grouparticleteaching–learning–based optimizationconvergence operatorbenchmark problemshumanoid robotfuzzy controlEngineering (General). Civil engineering (General)TA1-2040ENCogent Engineering, Vol 6, Iss 1 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	teaching–learning–based optimization convergence operator benchmark problems humanoid robot fuzzy control Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	teaching–learning–based optimization convergence operator benchmark problems humanoid robot fuzzy control Engineering (General). Civil engineering (General) TA1-2040 M. J. Mahmoodabadi R. Ostadzadeh CTLBO: Converged teaching–learning–based optimization
description	Teaching–learning–based optimization (TLBO) is an algorithm based on the influence of a teacher on the output of learners in a class. This method has shown to be more effective and efficient than other optimizations in finding the maximum solutions. In this paper, a new improved version of TLBO algorithm, called the converged teaching-learning-based optimization (CTLBO), is presented. In fact, it combines a proposed convergence operator with the teacher phase to find better solutions with a higher convergence rate. The method is tested on some benchmark problems and the results are compared with the original TLBO and other popular evolutionary algorithms. Furthermore, the introduced algorithm is used for optimization of fuzzy tracking control of a walking humanoid robot. In elaboration, fuzzy tracking control, which has appropriate membership functions and error indices, is employed in this paper as a promising intelligent approach to control the nonlinear dynamics of a humanoid robot. Summation of integrals of absolute angle errors and absolute control efforts is regarded as the objective function addressed by both TLBO and CTLBO algorithms in the present investigation.
format	article
author	M. J. Mahmoodabadi R. Ostadzadeh
author_facet	M. J. Mahmoodabadi R. Ostadzadeh
author_sort	M. J. Mahmoodabadi
title	CTLBO: Converged teaching–learning–based optimization
title_short	CTLBO: Converged teaching–learning–based optimization
title_full	CTLBO: Converged teaching–learning–based optimization
title_fullStr	CTLBO: Converged teaching–learning–based optimization
title_full_unstemmed	CTLBO: Converged teaching–learning–based optimization
title_sort	ctlbo: converged teaching–learning–based optimization
publisher	Taylor & Francis Group
publishDate	2019
url	https://doaj.org/article/379f0f639f0d47a4a1084b78ed0faee0
work_keys_str_mv	AT mjmahmoodabadi ctlboconvergedteachinglearningbasedoptimization AT rostadzadeh ctlboconvergedteachinglearningbasedoptimization
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CTLBO: Converged teaching–learning–based optimization

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