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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\u2019s 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.<\/jats:p>","DOI":"10.1155\/2017\/3295769","type":"journal-article","created":{"date-parts":[[2017,1,3]],"date-time":"2017-01-03T21:00:46Z","timestamp":1483477246000},"page":"1-16","source":"Crossref","is-referenced-by-count":43,"title":["Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding"],"prefix":"10.1155","volume":"2017","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5676-7636","authenticated-orcid":true,"given":"Linguo","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"},{"name":"College of Information Engineering, Fuyang Normal University, Fuyang 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