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dc.contributor.authorHan, C.
dc.contributor.authorWang, L.
dc.contributor.authorZhang, Z.
dc.contributor.authorXie, J.
dc.contributor.authorXing, Z.
dc.date.accessioned2018-06-18T10:29:49Z
dc.date.available2018-06-18T10:29:49Z
dc.date.issued2017-12cs
dc.identifier.citationRadioengineering. 2017 vol. 26, č. 4, s. 1048-1059. ISSN 1210-2512cs
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/82935
dc.description.abstractIn this paper, we propose an improved nondominated sorting genetic algorithm-II with scope constrained (INSGA-II/SC) with three modifications, which are dynamic nondomination strategy, scope-constrained strategy, and front uniformly distributed strategy. Here, the metric for multiobjective optimization mainly focuses on the computation complexity, convergence, and diversity of the final solutions. For a large search space in the initial process and a fast convergence in the last process, dynamic nondomination factor is considered in the rank operator. We can find a manageable number of Pareto solutions that are in the constrained scope instead of the entire Pareto front (PF) to reduce the computation complexity by scope-constrained strategy. In order to obtain a high performance for good representatives of the entire PF, the solutions closer to the uniformly distributed points on the current front will be chosen. In this paper, the proposed methods and two efficient multiobjective optimization methods are used for the optimization of mathematical problems and array pattern synthesis with lower side lobe level (SLL) and null. Numerical examples show that INSGA-II/SC has a high performance of diversity and convergence for the final solutions when compared with the other techniques published in the literature.en
dc.formattextcs
dc.format.extent1048-1059cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttp://www.radioeng.cz/fulltexts/2017/17_01_1048_1059.pdfcs
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectMultiobjective optimizationen
dc.subjectconvergenceen
dc.subjectdiversityen
dc.subjectarray pattern synthesisen
dc.subjectgenetic algorithmen
dc.titleLinear Array Pattern Synthesis Using An Improved Multiobjective Genetic Algorithmen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
dc.coverage.issue4cs
dc.coverage.volume26cs
dc.identifier.doi10.13164/re.2017.1048en
dc.rights.accessopenAccessen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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Creative Commons Attribution 4.0 International
Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0 International