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A Membrane-Inspired Evolutionary Algorithm with a Population P System and its Application to Distribution System Recon guration


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dc.creator Zhang, Gexiang
dc.creator Gutiérrez Naranjo, Miguel Ángel
dc.creator Qin, Yanhui
dc.creator Gheorgue, Marian 2016-02-05T08:24:20Z 2016-02-05T08:24:20Z 2012
dc.identifier.isbn 978-84-940056-6-4 es
dc.description.abstract This paper develops a membrane-inspired evolutionary algorithm, PSMA, which is designed by using a population P system and a quantum-inspired evolutionary algorithm (QIEA). We use a population P system with three cells to organize three types of QIEAs, where communications between cells are performed at the level of genes, instead of the level of individuals reported in the existing membrane algorithms in the literature. Knapsack problems are applied to discuss the parameter setting and to test the effectiveness of PSMA. Experimental results show that PSMA is superior to four representative QIEAs and our previous work with respect to the quality of solutions and the elapsed time. We also use PSMA to solve the optimal distribution system reconfiguration problem in power systems for minimizing the power loss. es
dc.description.sponsorship Junta de Andalucía P08-TIC-04200
dc.description.sponsorship Ministerio de Ciencia e Innovación TIN-2009-13192
dc.format application/pdf es
dc.language.iso eng es
dc.publisher Fénix Editora es
dc.relation.ispartof Proceedings of the Tenth Brainstorming Week on Membrane Computing, (2)277-292. Sevilla, E.T.S. de Ingeniería Informática, 30 de Enero-3 de Febrero, 2012, es
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 Internacional *
dc.rights.uri *
dc.subject Membrane computing es
dc.subject membrane-inspired evolutionary algorithm es
dc.subject population P system es
dc.subject distribution system reconfiguration es
dc.title A Membrane-Inspired Evolutionary Algorithm with a Population P System and its Application to Distribution System Recon guration es
dc.type info:eu-repo/semantics/conferenceObject es
dc.type.version info:eu-repo/semantics/publishedVersion es
dc.rights.accessrights info:eu-repo/semantics/openAccess es
dc.contributor.affiliation Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial es
dc.relation.projectID TIN-2009-13192
dc.relation.projectID P08-TIC-04200 Universidad de Sevilla. TIC193: Computación Natural
dc.contributor.funder Junta de Andalucía
dc.contributor.funder Ministerio de Ciencia e Innovación (MICIN). España
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