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The new algorithm first attempts to propose the adaptive pheromone concentration at the initial time and the adaptive global updating rules, which uses the double adaptive mechanism to automatically select the generalized fuzzy entropy parameters. The threshold of the image is obtained by introducing the parameters into the complement of the generalized fuzzy entropy, and then the optimal segmentation of the image is obtained. Compared with the existing image thresholding segmentation algorithms, in most cases, simulating results indicate that the new algorithm has less background information and clearer target information. In addition, it is superior to the existing algorithms in performance and greatly improves the stability and convergence speed.<\/jats:p>","DOI":"10.3233\/jifs-171643","type":"journal-article","created":{"date-parts":[[2018,8,5]],"date-time":"2018-08-05T06:39:31Z","timestamp":1533451171000},"page":"1979-1990","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Image thresholding segmentation of generalized fuzzy entropy based on double adaptive ant colony algorithm"],"prefix":"10.1177","volume":"35","author":[{"given":"Shengtao","family":"Jiang","sequence":"first","affiliation":[{"name":"Xidian University, School of Mathematics and Statistics, Xi\u2019an, China"}]},{"given":"Xuewen","family":"Mu","sequence":"additional","affiliation":[{"name":"Xidian University, School of Mathematics and Statistics, Xi\u2019an, China"}]},{"given":"Huan","family":"Cheng","sequence":"additional","affiliation":[{"name":"Xidian University, School of Mathematics and Statistics, Xi\u2019an, China"}]},{"given":"Qiyue","family":"Song","sequence":"additional","affiliation":[{"name":"Xidian University, School of Mathematics and Statistics, Xi\u2019an, China"}]}],"member":"179","published-online":{"date-parts":[[2018,7,31]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1117\/1.1631315"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-1684(98)00239-4"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0019-9958(65)90241-X"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/3477.658574"},{"issue":"10","key":"e_1_3_2_6_2","first-page":"1611","article-title":"Construction of generalized fuzzy entropy and its application in image segmentation, &","volume":"29","author":"Wu C.M.","year":"2007","unstructured":"WuC.M., , Construction of generalized fuzzy entropy and its application in image segmentation, &, Electronics 29(10) (2007), 1611\u20131614.","journal-title":"Electronics"},{"issue":"8","key":"e_1_3_2_7_2","first-page":"1865","article-title":"A generalized fuzzy entropy thresholding segmentation method based on the sugeno complement operator","volume":"30","author":"Fan J.L.","year":"2008","unstructured":"FanJ.L., , A generalized fuzzy entropy thresholding segmentation method based on the sugeno complement operator, Information Technology 30(8) (2008), 1865\u20131868.","journal-title":"Information Technology"},{"issue":"15","key":"e_1_3_2_8_2","first-page":"5476","article-title":"Unified model of fuzzy clustering algorithm based on entropy and its application to image segmentation","volume":"7","author":"Li K.","year":"2011","unstructured":"LiK., MaH. and WangY., Unified model of fuzzy clustering algorithm based on entropy and its application to image segmentation, Journal of Computational Information Systems 7(15) (2011), 5476\u20135483.","journal-title":"Journal of Computational Information Systems"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.4304\/jcp.9.7.1678-1683"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.05.025"},{"key":"e_1_3_2_11_2","first-page":"245","article-title":"Unsupervised hierarchical image segmentation through fuzzy entropy maximization","author":"Yin S.B.","year":"2017","unstructured":"YinS.B., QianY.M. and GongM.L., Unsupervised hierarchical image segmentation through fuzzy entropy maximization, Elsevier Science Inc (2017), pp. 245\u2013259.","journal-title":"Elsevier Science Inc"},{"key":"e_1_3_2_12_2","doi-asserted-by":"crossref","unstructured":"TaoW.B. 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