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dc.contributor.authorLazaridis, PI-
dc.contributor.authorTziris, E-
dc.contributor.authorZaharis, ZD-
dc.contributor.authorXenos, T-
dc.contributor.authorHolmes, V-
dc.contributor.authorCosmas, JP-
dc.contributor.authorGlover, I-
dc.identifier.citationProceedings of URSI Asia-Pacific Radio Science Conference, URSI AP-RASC, 21-25 August 2016, Seoul-Korea, pp. 1299 - 1301, (2016)en_US
dc.description.abstractBroadcasting antenna array optimized design involves gain maximization, main lobe down-tilting and null filling. In this study some of the most powerful evolutionary optimization algorithms are applied to this challenging problem: Differential Evolution, Particle Swarm, Invasive Weed, Adaptive Invasive Weed, and the Taguchi method. Evolutionary algorithms use a random search approach together with mechanisms inspired by biological evolution in order to iteratively improve the precision of randomly obtained solutions. Evolutionary algorithms are shown to require very substantial computational resources due to their random search nature. However, they are also very robust in finding a quasi-optimum solution by optimizing an appropriate fitness function. It is demonstrated that the algorithm producing the best fitness, and thus the best solution to the antenna problem, is Invasive Weed Optimization (IWO), followed by Particle Swarm Optimization (PSO) and Differential Evolution (DE), in second place and with similar results.en_US
dc.format.extent1299 - 1301-
dc.subjectAntenna arraysen_US
dc.subjectAntenna radiatio patternen_US
dc.subjectDifferential evolutionen_US
dc.subjectEvolutionary optimization alorithmsen_US
dc.subjectInvasive weed optimizationen_US
dc.subjectParticle swarmen_US
dc.titleComparative study of broadcasting antenna array optimization using evolutionary algorithmsen_US
dc.typeConference Paperen_US
dc.relation.isPartOf2016 URSI Asia-Pacific Radio Science Conference, URSI AP-RASC 2016-
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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