An improved bat algorithm and its application in multi-level image segmentation

Article type: Research Article

Authors: Yue, Xiaofeng^{; *} | Zhang, Hongbo

Affiliations: School of Mechatronic Engineering, Changchun University of Technology, Changchun, PR China

Correspondence: [*] Corresponding author. School of Mechatronic Engineering, Changchun University of Technology, Changchun, 130012 PR China. E-mail: yuexf@ccut.edu.cn

Abstract: As one of the most popular image segmentation techniques, multi-level thresholding is widely used. Much work has been done to improve the efficiency of multi-level thresholding, but satisfied affect is hard to achieve. In this paper, a multi-level image segmentation method using between-class variance (Otsu) based on improved bat algorithm (DWBA) with dynamically adjusting inertia weight and velocity stratification theory is proposed. DWBA algorithm has strong global search ability at the beginning. Then, the local search capability is enhanced with numbers increasing of iterations. More importantly, the performance of DWBA further improved, because bats with different fitness values have diverse velocities. Furthermore, an improved local search strategy is proposed to avoid the current best solution being replaced during iterations. The experimental results established that the proposed DWBA algorithm obtains better outcome than other algorithms.

Keywords: Multi-level image segmentation, DWBA algorithm, dynamically adjusting inertia weight, velocity stratification theory, Otsu

DOI: 10.3233/JIFS-182806

Journal: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 1399-1413, 2019