期刊
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
卷 2017, 期 -, 页码 -出版社
HINDAWI LTD
DOI: 10.1155/2017/3295769
关键词
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资金
- National Natural Science Foundation of China [61300239, 71301081, 61572261]
- Postdoctoral Science Foundation [2014M551635, 1302085B]
- Innovation Project of Graduate Students Foundation of Jiangsu Province [KYLX150841]
- Higher Education Revitalization Plan Foundation of Anhui Province [2013SQRL102ZD, 2015xdjy196, 2015jyxm728, 2016jyxm0774, 2016jyxm0777]
- Natural Science Fund for Colleges and Universities in Jiangsu Province [16KJB520034]
- Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
- Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET)
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.
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