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Dynamic social behavior algorithm for real-parameter optimization problems and optimization of hyper beamforming of linear antenna arrays

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dc.citedby3
dc.contributor.authorPrajindra�Sankar K.en_US
dc.contributor.authorKiong T.S.en_US
dc.contributor.authorSiaw�Paw J.K.en_US
dc.contributor.authorid36053261400en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid22951210700en_US
dc.date.accessioned2023-05-29T06:37:55Z
dc.date.available2023-05-29T06:37:55Z
dc.date.issued2017
dc.descriptionAnimals; Antenna arrays; Antennas; Beam forming networks; Beamforming; Behavioral research; Bioinformatics; Evolutionary algorithms; Global optimization; Problem solving; Swarm intelligence; Bio-inspired algorithms; Co-operative behaviors; Global optimization problems; Meta heuristics; Optimization algorithms; Optimization techniques; Real-parameter optimization; Swarm algorithms; Optimizationen_US
dc.description.abstractThe ever evolving complexity of real-world problems had become an impetus for the development of many new and efficient optimization algorithms. Meta-heuristics based on evolutionary computation and swarm intelligence are successful examples of nature-inspired optimization techniques. In this work, a new Dynamic Social Behavior (DSB) algorithm is proposed to solve global optimization problems. The DSB algorithm is based on the simulation of cooperative behavior of animal groups. In the proposed algorithm, individuals emulate the interaction of individuals based on biological laws of cooperative colony. This algorithm partially adopts the foraging strategy of animal groups and utilizes recruitment signal as a means of information transfer among individuals. In order to illustrate the proficiency and robustness of the proposed algorithm, it is compared with other well-known evolutionary algorithms. The comparison examines several series of widely used benchmark functions and an engineering problem on hyper beamforming optimization. The results testifies the superior performance of DSB compared with other state-of-the-art meta-heuristics. � 2017 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.engappai.2017.06.027
dc.identifier.epage414
dc.identifier.scopus2-s2.0-85026397516
dc.identifier.spage401
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85026397516&doi=10.1016%2fj.engappai.2017.06.027&partnerID=40&md5=2b9526126de4d360e17c75e13fcb938f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23134
dc.identifier.volume64
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleEngineering Applications of Artificial Intelligence
dc.titleDynamic social behavior algorithm for real-parameter optimization problems and optimization of hyper beamforming of linear antenna arraysen_US
dc.typeArticleen_US
dspace.entity.typePublication
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