title: Improved bat algorithm applied to multilevel image thresholding.
abstract: 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.
authors.affiliation: Faculty of Mathematics, University of Sarajevo, 71000 Sarajevo, Bosnia And Herzegovina.
authors.forename: Adis
authors.identifier: 0000-0003-0761-1667
authors.initials: A
authors.lastname: Alihodzic
authors.affiliation: Faculty of Computer Science, Megatrend University Belgrade, 11070 Belgrade, Serbia.
authors.forename: Milan
authors.identifier: 0000-0003-3794-3056
authors.initials: M
authors.lastname: Tuba
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D057386:Search Engine