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Stochastic leader gravitational search algorithm for enhanced adaptive beamforming technique

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dc.citedby4
dc.contributor.authorDarzi S.en_US
dc.contributor.authorIslam M.T.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorKibria S.en_US
dc.contributor.authorSingh M.en_US
dc.contributor.authorid55651612500en_US
dc.contributor.authorid55328836300en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid55637259500en_US
dc.contributor.authorid13105366200en_US
dc.date.accessioned2023-05-29T05:59:40Z
dc.date.available2023-05-29T05:59:40Z
dc.date.issued2015
dc.descriptioncontrolled clinical trial; human; leadership; randomization; randomized controlled trial; stochastic model; algorithm; microwave radiation; procedures; search engine; theoretical model; wireless communication; Algorithms; Microwaves; Models, Theoretical; Random Allocation; Search Engine; Wireless Technologyen_US
dc.description.abstractIn this paper, stochastic leader gravitational search algorithm (SL-GSA) based on randomized k is proposed. Standard GSA (SGSA) utilizes the best agents without any randomization, thus it is more prone to converge at suboptimal results. Initially, the new approach randomly choses k agents from the set of all agents to improve the global search ability. Gradually, the set of agents is reduced by eliminating the agents with the poorest performances to allow rapid convergence. The performance of the SL-GSA was analyzed for six well-known benchmark functions, and the results are compared with SGSA and some of its variants. Furthermore, the SLGSA is applied to minimum variance distortionless response (MVDR) beamforming technique to ensure compatibility with real world optimization problems. The proposed algorithmdemonstrates superior convergence rate and quality of solution for both real world problems and benchmark functions compared to original algorithm and other recent variants of SGSA. � 2015 Darzi et al.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNoe0140526
dc.identifier.doi10.1371/journal.pone.0140526
dc.identifier.issue11
dc.identifier.scopus2-s2.0-84952705618
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84952705618&doi=10.1371%2fjournal.pone.0140526&partnerID=40&md5=20502999c5a21a8108c23e2ca73ca468
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22217
dc.identifier.volume10
dc.publisherPublic Library of Scienceen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitlePLoS ONE
dc.titleStochastic leader gravitational search algorithm for enhanced adaptive beamforming techniqueen_US
dc.typeArticleen_US
dspace.entity.typePublication
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