{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T02:18:49Z","timestamp":1770776329780,"version":"3.50.0"},"reference-count":22,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2017,1,13]],"date-time":"2017-01-13T00:00:00Z","timestamp":1484265600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems: Applications in Engineering and Technology"],"published-print":{"date-parts":[[2017,1,13]]},"abstract":"<jats:p>\n                    Possibilistic fuzzy c-means (PFCM) is used for solving the problem of data classification. It relies on initial cluster centers set by users that are lack of theoretical supporting. Inappropriate initial values may result in deviation of cluster centers. In this paper, A\n                    <jats:italic toggle=\"yes\">Robust Adaptive Particle Swarm Optimization<\/jats:italic>\n                    based on steepest descent method is proposed to solve the problem of initialization and improving the performance of clustering. Combined with clustering algorithm, particle swarm optimization (PSO) possesses the good robustness to noises. Furthermore, since traditional PSO is inefficient when searching in the complex nonlinear hyperspace. Steepest descend method is applied to adaptively adjusting parameters. Moreover, optimum combined position is used to update the current information of each particle, which can discover more useful information lies in personal optimal experience and global optimal experience. The performance of proposed algorithm are tested in numerical simulations. The effectiveness, accuracy and stability of the new model are verified by simulations both with and without noises.\n                  <\/jats:p>","DOI":"10.3233\/jifs-141880","type":"journal-article","created":{"date-parts":[[2017,1,17]],"date-time":"2017-01-17T12:56:22Z","timestamp":1484657782000},"page":"23-33","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["A robust adaptive particle swarm optimization for clustering analysis based\u00a0on steepest descent method"],"prefix":"10.1177","volume":"32","author":[{"given":"Zhichao","family":"Sun","sequence":"first","affiliation":[{"name":"University of Electronic Science and Technology of China","place":["China"]}]},{"given":"Ying","family":"He","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China","place":["China"]}]},{"given":"Junjie","family":"Wu","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China","place":["China"]}]},{"given":"Yulin","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China","place":["China"]}]},{"given":"Jianyu","family":"Yang","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China","place":["China"]}]}],"member":"179","published-online":{"date-parts":[[2017,1,13]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2004.840099"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-0450-1"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1142\/S0218001413550057"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/91.227387"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/91.531780"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.3233\/IFS-2010-0453"},{"key":"e_1_3_2_8_2","first-page":"39","article-title":"A new optimizer using particle swarm theory","author":"Eberhart R.","year":"1995","unstructured":"EberhartR. and KennedyJ., A new optimizer using particle swarm theory, In Proceedings of International Symposium on Micro Machine and Human Science, IEEE, 1995, pp. 39\u201343.","journal-title":"In Proceedings of International Symposium on Micro Machine and Human Science"},{"key":"e_1_3_2_9_2","volume-title":"Pattern recognition with fuzzy objective function algorithms","author":"Bezdek J.C.","year":"2013","unstructured":"BezdekJ.C., Pattern recognition with fuzzy objective function algorithms, Springer Science & Business Media, 2013."},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/0020-0255(73)90043-1"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1142\/S0218001405004447"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/91.531779"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.3233\/IFS-131020"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.3233\/IFS-130892"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2004.830335"},{"key":"e_1_3_2_16_2","first-page":"760","volume-title":"Encyclopedia of Machine Learning","author":"Kennedy J.","year":"2010","unstructured":"KennedyJ., Particle swarm optimization, In Encyclopedia of Machine Learning, Springer, 2010, pp. 760\u2013766."},{"key":"e_1_3_2_17_2","first-page":"69","article-title":"A modified particle swarm optimizer","author":"Shi Y.","year":"1998","unstructured":"ShiY. and EberhartR., A modified particle swarm optimizer, In Proceedings of IEEE International Conference on Evolutionary Computation 1998, IEEE, 1998, pp. 69\u201373.","journal-title":"Proceedings of IEEE International Conference on Evolutionary Computation 1998"},{"key":"e_1_3_2_18_2","article-title":"Empirical study of particle swarm optimization","volume":"3","author":"Shi Y.","year":"1999","unstructured":"ShiY. and EberhartR.C., Empirical study of particle swarm optimization, In Proc IEEE Cong Evolutionary Computation, volume 3. IEEE, 1999.","journal-title":"Proc IEEE Cong Evolutionary Computation"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/4235.985692"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.3233\/IFS-2006-00287"},{"key":"e_1_3_2_21_2","first-page":"065","article-title":"Particle swarm optimization algorithm based on non-symmetric learning factor adjusting","volume":"19","author":"Mao K.-F.","year":"2010","unstructured":"MaoK.-F., BaoG.-Q. and ChiX., Particle swarm optimization algorithm based on non-symmetric learning factor adjusting, Computer Engineering 19 (2010), 065.","journal-title":"Computer Engineering"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2013.2296151"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2004.826071"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems: Applications in Engineering and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-141880","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-141880","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-141880","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T01:24:39Z","timestamp":1770513879000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-141880"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,1,13]]},"references-count":22,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,1,13]]}},"alternative-id":["10.3233\/JIFS-141880"],"URL":"https:\/\/doi.org\/10.3233\/jifs-141880","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,1,13]]}}}