{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T00:43:32Z","timestamp":1778892212046,"version":"3.51.4"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100005046","name":"Natural Science Foundation of Heilongjiang Province","doi-asserted-by":"crossref","award":["F2018019"],"award-info":[{"award-number":["F2018019"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2020,11]]},"DOI":"10.1007\/s00521-018-03977-x","type":"journal-article","created":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T03:42:05Z","timestamp":1546314125000},"page":"16891-16899","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["An adaptive artificial-fish-swarm-inspired fuzzy C-means algorithm"],"prefix":"10.1007","volume":"32","author":[{"given":"Liang","family":"Xi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengbin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,1,1]]},"reference":[{"issue":"5","key":"3977_CR1","doi-asserted-by":"publisher","first-page":"1573","DOI":"10.1007\/s00521-016-2765-y","volume":"30","author":"A Amirkhani","year":"2018","unstructured":"Amirkhani A, Mosavi MR, Mohammadi K et al (2018) A novel hybrid method based on fuzzy cognitive maps and fuzzy clustering algorithms for grading celiac disease. Neural Comput Appl 30(5):1573\u20131588","journal-title":"Neural Comput Appl"},{"key":"3977_CR2","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.knosys.2017.11.010","volume":"141","author":"X Yu","year":"2018","unstructured":"Yu X, Chu Y, Jiang F et al (2018) SVMs classification based two-side cross domain collaborative filtering by inferring intrinsic user and item features. Knowl-Based Syst 141:80\u201391","journal-title":"Knowl-Based Syst"},{"issue":"5","key":"3977_CR3","doi-asserted-by":"publisher","first-page":"1679","DOI":"10.1007\/s00521-016-2817-3","volume":"30","author":"R Katarya","year":"2018","unstructured":"Katarya R, Verma OP (2018) Recommender system with grey wolf optimizer and FCM. Neural Comput Appl 30(5):1679\u20131687","journal-title":"Neural Comput Appl"},{"issue":"8","key":"3977_CR4","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s00521-016-2712-y","volume":"29","author":"S Demircan","year":"2018","unstructured":"Demircan S, Kahramanli H (2018) Application of fuzzy C-means clustering algorithm to spectral features for emotion classification from speech. Neural Comput Appl 29(8):59\u201366","journal-title":"Neural Comput Appl"},{"issue":"4","key":"3977_CR5","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1007\/s13198-017-0681-x","volume":"9","author":"N Bharill","year":"2018","unstructured":"Bharill N, Patel OP, Tiwari A (2018) Quantum-inspired evolutionary approach for selection of optimal parameters of fuzzy clustering. Int J Syst Assur Eng Manag 9(4):875\u2013887","journal-title":"Int J Syst Assur Eng Manag"},{"issue":"6","key":"3977_CR6","doi-asserted-by":"publisher","first-page":"2940","DOI":"10.1109\/JSTARS.2017.2694439","volume":"10","author":"F Kowkabi","year":"2017","unstructured":"Kowkabi F, Keshavarz Ahmad Ghassemian H (2017) Hybrid preprocessing algorithm for endmember extraction using clustering, over-segmentation, and local entropy criterion. IEEE J Sel Top Appl Earth Obs Remote Sens 10(6):2940\u20132949","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"3977_CR7","doi-asserted-by":"publisher","first-page":"S247","DOI":"10.1007\/s00521-013-1350-x","volume":"23","author":"Z Zainuddin","year":"2013","unstructured":"Zainuddin Z, Ong P (2013) Design of wavelet neural networks based on symmetry fuzzy C-means for function approximation. Neural Comput Appl 23:S247\u2013S259","journal-title":"Neural Comput Appl"},{"issue":"4","key":"3977_CR8","doi-asserted-by":"publisher","first-page":"1979","DOI":"10.1007\/s11277-016-3340-7","volume":"94","author":"P Sengottuvelan","year":"2017","unstructured":"Sengottuvelan P, Prasath N (2017) BAFSA: breeding artificial fish swarm algorithm for optimal cluster head selection in wireless sensor networks. Wireless Pers Commun 94(4):1979\u20131991","journal-title":"Wireless Pers Commun"},{"key":"3977_CR9","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.egypro.2016.11.175","volume":"90","author":"KP Kumar","year":"2016","unstructured":"Kumar KP, Saravanan B, Swarup KS (2016) Optimization of renewable energy sources in a microgrid using artificial fish swarm algorithm. Energy Proced 90:107\u2013113","journal-title":"Energy Proced"},{"key":"3977_CR10","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.jappgeo.2016.03.027","volume":"129","author":"L Liu","year":"2016","unstructured":"Liu L, Sun SZ, Yu H et al (2016) A modified fuzzy C-means (FCM) clustering algorithm and its application on carbonate fluid identification. J Appl Geophys 129:28\u201335","journal-title":"J Appl Geophys"},{"issue":"5","key":"3977_CR11","first-page":"1123","volume":"39","author":"MS Xiao","year":"2017","unstructured":"Xiao MS, Xiao Z, Wen ZC et al (2017) Improved FCM clustering algorithm based on spatial correlation and membership smoothing. J Electron Inf Technol 39(5):1123\u20131129","journal-title":"J Electron Inf Technol"},{"issue":"3","key":"3977_CR12","first-page":"687","volume":"45","author":"HP Chen","year":"2017","unstructured":"Chen HP, Shen XJ, Long JW et al (2017) Fuzzy clustering algorithm for automatic identification of clusters. Acta Electron Sin 45(3):687\u2013694","journal-title":"Acta Electron Sin"},{"key":"3977_CR13","doi-asserted-by":"publisher","first-page":"S279","DOI":"10.1007\/s00521-013-1394-y","volume":"23","author":"I Shanthi","year":"2013","unstructured":"Shanthi I, Valarmathi ML (2013) SAR image despeckling using possibilistic fuzzy C-means clustering and edge detection in bandelet domain. Neural Comput Appl 23:S279\u2013S291","journal-title":"Neural Comput Appl"},{"issue":"4","key":"3977_CR14","doi-asserted-by":"publisher","first-page":"1035","DOI":"10.1109\/TKDE.2015.2507130","volume":"28","author":"DM Johnson","year":"2016","unstructured":"Johnson DM, Xiong CM, Corso JJ (2016) Semi-supervised nonlinear distance metric learning via forests of max-margin cluster hierarchies. IEEE Trans Knowl Data Eng 28(4):1035\u20131046","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"3977_CR15","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.compbiomed.2012.10.002","volume":"43","author":"SR Kannan","year":"2013","unstructured":"Kannan SR (2013) Effective FCM noise clustering algorithms in medical images. Comput Biol Med 43(2):73\u201383","journal-title":"Comput Biol Med"},{"key":"3977_CR16","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.swevo.2013.09.002","volume":"14","author":"MAK Azad","year":"2014","unstructured":"Azad MAK, Rocha AMAC, Fernandes EMGP (2014) Improved binary artificial fish swarm algorithm for the 0-1 multidimensional knapsack problems. Swarm Evolut Comput 14:66\u201375","journal-title":"Swarm Evolut Comput"},{"key":"3977_CR17","doi-asserted-by":"publisher","first-page":"522","DOI":"10.1016\/j.neucom.2015.06.090","volume":"174","author":"XY Luan","year":"2016","unstructured":"Luan XY, Li ZP, Liu TZ (2016) A novel attribute reduction algorithm based on rough set and improved artificial fish swarm algorithm. Neurocomputing 174:522\u2013529","journal-title":"Neurocomputing"},{"key":"3977_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-017-1182-z","author":"RPS Manikandan","year":"2017","unstructured":"Manikandan RPS, Kalpana AM (2017) Feature selection using fish swarm optimization in big data. Cluster Computing. \n                  https:\/\/doi.org\/10.1007\/s10586-017-1182-z","journal-title":"Cluster Computing"},{"issue":"9","key":"3977_CR19","doi-asserted-by":"publisher","first-page":"2667","DOI":"10.1007\/s00500-014-1436-0","volume":"19","author":"SA El-said","year":"2015","unstructured":"El-said SA (2015) Image quantization using improved artificial fish swarm algorithm. Soft Comput 19(9):2667\u20132679","journal-title":"Soft Comput"},{"issue":"24","key":"3977_CR20","first-page":"169","volume":"36","author":"LG Wang","year":"2010","unstructured":"Wang LG, Shi QH (2010) Parameters analysis of artificial fish swarm algorithm. Comput Eng 36(24):169\u2013171","journal-title":"Comput Eng"},{"issue":"1","key":"3977_CR21","first-page":"1","volume":"35","author":"XM Ma","year":"2014","unstructured":"Ma XM, Liu N (2014) Improved artificial fish-swarm algorithm based on adaptive vision for solving the shortest path problem. J Commun 35(1):1\u20136","journal-title":"J Commun"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-03977-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-018-03977-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-03977-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,25]],"date-time":"2020-10-25T13:03:33Z","timestamp":1603631013000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-018-03977-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,1]]},"references-count":21,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2020,11]]}},"alternative-id":["3977"],"URL":"https:\/\/doi.org\/10.1007\/s00521-018-03977-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,1]]},"assertion":[{"value":"24 October 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 December 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 January 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}