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...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: M. J. Mahmoodabadi, R. Ostadzadeh
Formato: article
Lenguaje:EN
Publicado: Taylor & Francis Group 2019
Materias:
Acceso en línea:https://doaj.org/article/379f0f639f0d47a4a1084b78ed0faee0
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Descripción
Sumario: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.