Nonlinear Inertia Weighted Teaching-Learning-Based Optimization for Solving Global Optimization Problem.
Comput Intell Neurosci
; 2015: 292576, 2015.
Article
em En
| MEDLINE
| ID: mdl-26421005
ABSTRACT
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 weighted factor to replace the original random number in teacher phase and learner phase. The proposed algorithm is tested on a number of benchmark functions, and its performance comparisons are provided against the basic TLBO and some other well-known optimization algorithms. The experiment results show that the proposed algorithm has a faster convergence rate and better performance than the basic TLBO and some other algorithms as well.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Resolução de Problemas
/
Ensino
/
Algoritmos
/
Inteligência Artificial
/
Dinâmica não Linear
/
Aprendizagem
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Comput Intell Neurosci
Assunto da revista:
INFORMATICA MEDICA
/
NEUROLOGIA
Ano de publicação:
2015
Tipo de documento:
Article
País de afiliação:
China