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The Scientific World Journal
Volume 2014 (2014), Article ID 136920, 17 pages
Research Article

Bare-Bones Teaching-Learning-Based Optimization

1School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
2School of Physics and Electronic Information, Huaibei Normal University, Huaibei 235000, China

Received 20 February 2014; Accepted 7 April 2014; Published 10 June 2014

Academic Editors: S. Balochian and Y. Zhang

Copyright © 2014 Feng Zou 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 which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.