Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/65752
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dc.contributor.authorLu, Shijie
dc.date.accessioned2015-12-11T06:02:57Z
dc.date.available2015-12-11T06:02:57Z
dc.date.copyright2015en_US
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/10356/65752
dc.description.abstractNowdays Evolutionary Optimization has recently experienced a remarkable growth. This report convers PSO algorithms and BAT algorithms. Both of them are started with a population which generated randomly and evaluate the population by using the fitness values. However PSO simulates the behaviors of bird flocking, BAT simulates the echolocation of microbats. And using MATLAB software implements the program to compare the two algorithms in Beamforming application. From the result, we can conclude that BAT algorithm is better than PSO algorithm, as BAT algorithm is more efficient and fast. Furthermore, some further improvements, suggestions and recommendation for the similar projects carried on in the future.en_US
dc.format.extent45 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Wireless communication systemsen_US
dc.titleEvolutionary optimization algorithms and their applications in wireless systemsen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLu Yilongen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US
dc.contributor.researchCentre for Advanced Information Systemsen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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