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Scientific Programming
Volume 2016 (2016), Article ID 8031560, 13 pages
Research Article

Modified Bat Algorithm Based on Lévy Flight and Opposition Based Learning

1School of Science, China University of Petroleum, Qingdao 266580, China
2College of Mechanical and Electronic Engineering, China University of Petroleum, Qingdao 266580, China
3School of Economics and Management, China University of Petroleum, Qingdao 266580, China

Received 14 July 2016; Accepted 25 October 2016

Academic Editor: Xiang Li

Copyright © 2016 Xian Shan 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.


Bat Algorithm (BA) is a swarm intelligence algorithm which has been intensively applied to solve academic and real life optimization problems. However, due to the lack of good balance between exploration and exploitation, BA sometimes fails at finding global optimum and is easily trapped into local optima. In order to overcome the premature problem and improve the local searching ability of Bat Algorithm for optimization problems, we propose an improved BA called OBMLBA. In the proposed algorithm, a modified search equation with more useful information from the search experiences is introduced to generate a candidate solution, and Lévy Flight random walk is incorporated with BA in order to avoid being trapped into local optima. Furthermore, the concept of opposition based learning (OBL) is embedded to BA to enhance the diversity and convergence capability. To evaluate the performance of the proposed approach, 16 benchmark functions have been employed. The results obtained by the experiments demonstrate the effectiveness and efficiency of OBMLBA for global optimization problems. Comparisons with some other BA variants and other state-of-the-art algorithms have shown the proposed approach significantly improves the performance of BA. Performances of the proposed algorithm on large scale optimization problems and real world optimization problems are not discussed in the paper, and it will be studied in the future work.