Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2015, Article ID 292576, 15 pages
Research Article

Nonlinear Inertia Weighted Teaching-Learning-Based Optimization for Solving Global Optimization Problem

1School of Mechanical and Precision Instrumental Engineering, Xi’an University of Technology, Xi’an, Shaanxi 710048, China
2School of Information Engineering, Tibet University for Nationalities, Xianyang, Shaanxi 712082, China

Received 28 June 2015; Revised 11 August 2015; Accepted 17 August 2015

Academic Editor: Michael Schmuker

Copyright © 2015 Zong-Sheng Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.