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The Scientific World Journal
Volume 2014, Article ID 176718, 16 pages
Research Article

Improved Bat Algorithm Applied to Multilevel Image Thresholding

1Faculty of Mathematics, University of Sarajevo, 71000 Sarajevo, Bosnia And Herzegovina
2Faculty of Computer Science, Megatrend University Belgrade, 11070 Belgrade, Serbia

Received 25 April 2014; Accepted 28 June 2014; Published 3 August 2014

Academic Editor: Xin-She Yang

Copyright © 2014 Adis Alihodzic and Milan Tuba. 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.


Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. In this paper, we adjusted one of the latest swarm intelligence algorithms, the bat algorithm, for the multilevel image thresholding problem. The results of testing on standard benchmark images show that the bat algorithm is comparable with other state-of-the-art algorithms. We improved standard bat algorithm, where our modifications add some elements from the differential evolution and from the artificial bee colony algorithm. Our new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed.