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Mathematical Problems in Engineering
Volume 2013, Article ID 413565, 29 pages
Research Article

An Improved Harmony Search Based on Teaching-Learning Strategy for Unconstrained Optimization Problems

1School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong, Shaanxi 723001, China
2School of Science, Ningxia Medical University, Yinchuan, Ningxia 750004, China

Received 22 August 2012; Accepted 12 November 2012

Academic Editor: Baozhen Yao

Copyright © 2013 Shouheng Tuo 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.


Harmony search (HS) algorithm is an emerging population-based metaheuristic algorithm, which is inspired by the music improvisation process. The HS method has been developed rapidly and applied widely during the past decade. In this paper, an improved global harmony search algorithm, named harmony search based on teaching-learning (HSTL), is presented for high dimension complex optimization problems. In HSTL algorithm, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to maintain the proper balance between convergence and population diversity, and dynamic strategy is adopted to change the parameters. The proposed HSTL algorithm is investigated and compared with three other state-of-the-art HS optimization algorithms. Furthermore, to demonstrate the robustness and convergence, the success rate and convergence analysis is also studied. The experimental results of 31 complex benchmark functions demonstrate that the HSTL method has strong convergence and robustness and has better balance capacity of space exploration and local exploitation on high dimension complex optimization problems.