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Aiming at the characteristics of particle swarm optimization (PSO) and genetic algorithm (GA) in optimization, a method of optimizing SVM parameters based on dynamic co-evolutionary algorithm (PSO-GA) is proposed. This method can dynamically adjust the selection probability of PSO and GA strategy, realize the complementarity of evolution between PSO and GA, improve the convergence speed and realize the optimization of depth and breadth. The experimental results show that the method improves the parameter selection efficiency of SVM, and the obtained parameters are optimal for the classification of the test samples.<\/jats:p>","DOI":"10.3233\/jifs-169593","type":"journal-article","created":{"date-parts":[[2018,6,5]],"date-time":"2018-06-05T14:31:33Z","timestamp":1528209093000},"page":"343-351","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Remote sensing image classification based on dynamic Co-evolutionary parameter optimization of SVM"],"prefix":"10.1177","volume":"35","author":[{"given":"Xiaodong","family":"Yu","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Harbin Engineering University, Harbin, China"},{"name":"College of Computer Science and Technology, Harbin Normal University, Harbin, China"}]},{"given":"Hongbin","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Harbin Engineering University, Harbin, China"}]}],"member":"179","published-online":{"date-parts":[[2018,6,5]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2009.05.079"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1117\/1.JRS.7.073597"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1012450327387"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2006.07.014"},{"key":"e_1_3_2_6_2","first-page":"454","article-title":"An overview of the research on coevolutionary algorithms","volume":"45","author":"Dong H.","year":"2008","unstructured":"DongH., HuangH. and G.Yin, An overview of the research on coevolutionary algorithms, Journal of Computer Research and Development45 (2008), 454\u2013463.","journal-title":"Journal of Computer Research and Development"},{"key":"e_1_3_2_7_2","first-page":"39","article-title":"A new optimizer using particle swarm theory","author":"Eberhart R.","year":"2002","unstructured":"EberhartR. and J.Kennedy, A new optimizer using particle swarm theory, International Symposium on MICRO Machine and Human Science IEEE (2002), 39\u201343.","journal-title":"International Symposium on MICRO Machine and Human Science IEEE"},{"key":"e_1_3_2_8_2","first-page":"1","article-title":"Remote sensing image classification based on minimum distance method","volume":"22","author":"Feng D.C.","year":"2012","unstructured":"FengD.C., Remote sensing image classification based on minimum distance method, Journal of North ChinaInstitute of Aerospace Engineering22 (2012), 1\u20133.","journal-title":"Journal of North ChinaInstitute of Aerospace Engineering"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-29124-1_19"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2013.6557618"},{"issue":"2011","key":"e_1_3_2_11_2","first-page":"1","article-title":"HybridPSO-SA type algorithms for multi-modal function optimization andreducing energy consumption in embedded systems","volume":"2011","author":"Idoumghar L.","unstructured":"IdoumgharL., Idrissi-AouadM., MelkemiM. and R.Schott, HybridPSO-SA type algorithms for multi-modal function optimization andreducing energy consumption in embedded systems, Journal of Applied Computational Intelligence and Soft Computing2011(2011), 1\u201312. doi: 10.1155\/2011\/138078","journal-title":"Journal of Applied Computational Intelligence and Soft Computing"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.proeng.2012.06.210"},{"key":"e_1_3_2_13_2","author":"Nasien D.","year":"2010","unstructured":"NasienD., YuhanizS.S., HaronH., Statistical learning theory and support vector machines, 2010.","journal-title":"Statistical learning theory and support vector machines"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2017.03.027"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/4235.585893"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2006.10.019"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2014.01.020"},{"key":"e_1_3_2_18_2","author":"Xu Z.","year":"2011","unstructured":"XuZ., Advances of support vector machines (svm)ss, Computer Science, 2011.","journal-title":"Advances of support vector machines (svm)ss, Computer Science"},{"key":"e_1_3_2_19_2","first-page":"321","article-title":"A modified genetic algorithm based on Real-Coding","volume":"26","author":"Ying D.","year":"2005","unstructured":"YingD., LiuH.J., Bao-DongX.U. and J.F.Tang, A modified genetic algorithm based on Real-Coding, Journalof Northeastern University (Natural Science)26 (2005), 321\u2013322.","journal-title":"Journalof Northeastern University (Natural Science)"},{"key":"e_1_3_2_20_2","unstructured":"ZarJ.H. 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