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Computational Intelligence and Neuroscience
Volume 2017, Article ID 3295769, 16 pages
Research Article

Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding

1School of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2College of Information Engineering, Fuyang Normal University, Fuyang 236041, China
3School of Internet of Things, Nanjing University of Posts and Telecommunication, Nanjing 210003, China

Correspondence should be addressed to Linguo Li; moc.361@2121-gll

Received 1 July 2016; Revised 21 November 2016; Accepted 6 December 2016; Published 3 January 2017

Academic Editor: Cheng-Jian Lin

Copyright © 2017 Linguo Li 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.


The computation of image segmentation has become more complicated with the increasing number of thresholds, and the option and application of the thresholds in image thresholding fields have become an NP problem at the same time. The paper puts forward the modified discrete grey wolf optimizer algorithm (MDGWO), which improves on the optimal solution updating mechanism of the search agent by the weights. Taking Kapur’s entropy as the optimized function and based on the discreteness of threshold in image segmentation, the paper firstly discretizes the grey wolf optimizer (GWO) and then proposes a new attack strategy by using the weight coefficient to replace the search formula for optimal solution used in the original algorithm. The experimental results show that MDGWO can search out the optimal thresholds efficiently and precisely, which are very close to the result examined by exhaustive searches. In comparison with the electromagnetism optimization (EMO), the differential evolution (DE), the Artifical Bee Colony (ABC), and the classical GWO, it is concluded that MDGWO has advantages over the latter four in terms of image segmentation quality and objective function values and their stability.