{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:40:33Z","timestamp":1777704033266,"version":"3.51.4"},"reference-count":29,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2018,7,23]],"date-time":"2018-07-23T00:00:00Z","timestamp":1532304000000},"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"],"published-print":{"date-parts":[[2018,8,26]]},"abstract":"<jats:p>Blind source separation (BSS) is an advanced method of signal processing. Essentially, the problem in BSS is to separate and estimate the original signal from the observed mixed signal source without knowing the characteristics of the original signal. Independent component analysis (ICA) is a popular approach for blind source separation, and because its traditional search scheme is based on a gradient algorithm, a convergence problem will arise. In order to overcome the defect, this paper proposed to apply Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) to conduct accelerated computing of the rate of convergence of a demixing matrix in ICA. However, the PSO converges prematurely, and the population diversity is reduced rapidly, so that the optimal solution falls into the local optimum. In order to increase the diversity of PSO, GPSO-based ICA algorithm (GPSO-ICA) is proposed that has the exploring ability of GSA, so that the ICA algorithm has a higher convergence rate and better ability to escape local optimization. A series of comparisons is implemented for the ICA algorithms based on PSO, GSA, and GPSO. The results show that GPSO-ICA has better performance than the other methods.<\/jats:p>","DOI":"10.3233\/jifs-171545","type":"journal-article","created":{"date-parts":[[2018,7,24]],"date-time":"2018-07-24T17:17:34Z","timestamp":1532452654000},"page":"1943-1957","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["GPSO-ICA: Independent Component Analysis based on Gravitational Particle Swarm Optimization for blind source separation"],"prefix":"10.1177","volume":"35","author":[{"given":"Shih-Hsiung","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C."}]},{"given":"Chu-Sing","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C."}]}],"member":"179","published-online":{"date-parts":[[2018,7,23]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","unstructured":"LeeT.W. Independent Component Analysis-Theory and Application Norwell MA: Kuwer 1998.","DOI":"10.1007\/978-1-4757-2851-4_2"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/0165-1684(94)90029-9"},{"key":"e_1_3_2_4_2","first-page":"895","article-title":"Independent Component Analysis for Parallel Financial Time Series","volume":"2","author":"Kiviluoto K.","year":"1998","unstructured":"KiviluotoK. and OjaE., Independent Component Analysis for Parallel Financial Time Series, In Proceedings of the International Conference on Neural information Processing (ICONIP \u201998), vol. 2, (1998), pp. 895\u2013898.","journal-title":"In Proceedings of the International Conference on Neural information Processing (ICONIP \u201998)"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSA.2005.858061"},{"key":"e_1_3_2_6_2","first-page":"94","article-title":"Survey on independent component analysis","volume":"2","author":"Hyvarinen A.","year":"1999","unstructured":"HyvarinenA., Survey on independent component analysis, Neural Comp Surveys 2 (1999), 94\u2013128.","journal-title":"Neural Comp Surveys"},{"key":"e_1_3_2_7_2","first-page":"145","volume-title":"Advances in Neural Information Processing System","author":"Makeig S.","year":"1996","unstructured":"MakeigS., BellA., JungT. and SejnowskiT., Independent Component Analysis of Electroencephalographic, Advances in Neural Information Processing System Cambridge: MA, MIT Press vol. 8, (1996) pp. 145\u2013151."},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/72.761722"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/97.923043"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1995.7.6.1129"},{"key":"e_1_3_2_11_2","doi-asserted-by":"crossref","unstructured":"LeeS.H. and YangC.S. PSO ICA with BRM for Image Enhancement Computer Consumer and Control (IS3C) 2016 International Symposium on 2016.","DOI":"10.1109\/IS3C.2016.87"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2007.898781"},{"key":"e_1_3_2_13_2","first-page":"1942","article-title":"Particle swarm optimization","volume":"4","author":"Kennedy J.","year":"1995","unstructured":"KennedyJ. and EberhartR., Particle swarm optimization. In IEEE International Conferenceon Neural Networks, volume 4 1995. pp. 1942\u20131948.","journal-title":"IEEE International Conferenceon Neural Networks"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2009.2030331"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2006.883272"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2010.02.032"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2013.05.003"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/CASSET.2004.1322981"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11721-007-0002-0"},{"key":"e_1_3_2_20_2","first-page":"26","article-title":"DNPSO: A Dynamic Niching Particle Swarm Optimizer for Multi-modal Optimization, In pp","author":"Nickabadi A.","year":"2008","unstructured":"NickabadiA., EbadzadehM.M. and SafabakhshR., DNPSO: A Dynamic Niching Particle Swarm Optimizer for Multi-modal Optimization, In pp, Proceedings of IEEE Congress on Evolutionary Computation (2008), 26\u201332.","journal-title":"Proceedings of IEEE Congress on Evolutionary Computation"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2005.857610"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2010.2052054"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2012.10.012"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2009.03.004"},{"key":"e_1_3_2_25_2","doi-asserted-by":"crossref","unstructured":"MirjaliliS. and HashimS.Z.M. A New Hybrid PSOGSA Algorithm for Function Optimization International Conference on Computer and Information Application 2010.","DOI":"10.1109\/ICCIA.2010.6141614"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.3233\/IFS-151543"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1016540724870"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(00)00026-5"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2005.05.020"},{"key":"e_1_3_2_30_2","doi-asserted-by":"crossref","unstructured":"NianF. LiW. SunX. and LiM. An Improved Particle Swarm Optimization Application to Independent Component Analysis Information Engineering and Computer Science 2009 ICIECS 2009 International Conference on 2009.","DOI":"10.1109\/ICIECS.2009.5364412"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-171545","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-171545","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-171545","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:40:15Z","timestamp":1777455615000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-171545"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,23]]},"references-count":29,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2018,8,26]]}},"alternative-id":["10.3233\/JIFS-171545"],"URL":"https:\/\/doi.org\/10.3233\/jifs-171545","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,23]]}}}