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Computational Intelligence and Neuroscience
Volume 2016, Article ID 8341275, 12 pages
Research Article

Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization

1State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
2College of Electronics and Information Engineering, South-Central University for Nationalities, Wuhan 430074, China

Received 6 August 2015; Revised 26 December 2015; Accepted 30 December 2015

Academic Editor: Leonardo Franco

Copyright © 2016 Xiangzhu He 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.


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 proposed algorithm outperforms other algorithms and achieves a satisfactory improvement over TLBO.