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Article: A novel switching local evolutionary PSO for quantitative analysis of lateral flow immunoassay

TitleA novel switching local evolutionary PSO for quantitative analysis of lateral flow immunoassay
Authors
KeywordsLateral flow immunoassay
Particle swarm optimization
Differential evolution
Non-homogeneous Markov chain
Immunochromatographic strip
Issue Date2014
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/eswa
Citation
Expert Systems with Applications, 2014, v. 41 n. 4, part. 2, p. 1708-1715 How to Cite?
AbstractThis paper presents a novel particle swarm optimization (PSO) based on a non-homogeneous Markov chain and differential evolution (DE) for quantification analysis of the lateral flow immunoassay (LFIA), which represents the first attempt to estimate the concentration of target analyte based on the well-established state-space model. A new switching local evolutionary PSO (SLEPSO) is developed and analyzed. The velocity updating equation jumps from one mode to another based on the non-homogeneous Markov chain, where the probability transition matrix is updated by calculating the diversity and current optimal solution. Furthermore, DE mutation and crossover operations are implemented to improve local best particles searching in PSO. Compared with some well-known PSO algorithms, the experiments results show the superiority of proposed SLEPSO. Finally, the new SLEPSO is successfully exploited to quantification analysis of the LFIA system, which is essentially nonlinear and dynamic. Therefore, this can provide a new method for the area of quantitative interpretation of LFIA system.
Persistent Identifierhttp://hdl.handle.net/10722/200607
ISSN
2017 Impact Factor: 3.768
2015 SCImago Journal Rankings: 1.839
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZeng, N-
dc.contributor.authorHung, YS-
dc.contributor.authorLi, Y-
dc.contributor.authorDu, M-
dc.date.accessioned2014-08-21T06:52:39Z-
dc.date.available2014-08-21T06:52:39Z-
dc.date.issued2014-
dc.identifier.citationExpert Systems with Applications, 2014, v. 41 n. 4, part. 2, p. 1708-1715-
dc.identifier.issn0957-4174-
dc.identifier.urihttp://hdl.handle.net/10722/200607-
dc.description.abstractThis paper presents a novel particle swarm optimization (PSO) based on a non-homogeneous Markov chain and differential evolution (DE) for quantification analysis of the lateral flow immunoassay (LFIA), which represents the first attempt to estimate the concentration of target analyte based on the well-established state-space model. A new switching local evolutionary PSO (SLEPSO) is developed and analyzed. The velocity updating equation jumps from one mode to another based on the non-homogeneous Markov chain, where the probability transition matrix is updated by calculating the diversity and current optimal solution. Furthermore, DE mutation and crossover operations are implemented to improve local best particles searching in PSO. Compared with some well-known PSO algorithms, the experiments results show the superiority of proposed SLEPSO. Finally, the new SLEPSO is successfully exploited to quantification analysis of the LFIA system, which is essentially nonlinear and dynamic. Therefore, this can provide a new method for the area of quantitative interpretation of LFIA system.-
dc.languageeng-
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/eswa-
dc.relation.ispartofExpert Systems with Applications-
dc.subjectLateral flow immunoassay-
dc.subjectParticle swarm optimization-
dc.subjectDifferential evolution-
dc.subjectNon-homogeneous Markov chain-
dc.subjectImmunochromatographic strip-
dc.titleA novel switching local evolutionary PSO for quantitative analysis of lateral flow immunoassay-
dc.typeArticle-
dc.identifier.emailHung, YS: yshung@hkucc.hku.hk-
dc.identifier.authorityHung, YS=rp00220-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.eswa.2013.08.069-
dc.identifier.scopuseid_2-s2.0-84888387813-
dc.identifier.hkuros232936-
dc.identifier.volume41-
dc.identifier.issue4, part. 2-
dc.identifier.spage1708-
dc.identifier.epage1715-
dc.identifier.isiWOS:000329955900019-
dc.publisher.placeUnited Kingdom-

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