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Exponential Log-Periodic Antenna Design Using Improved Particle Swarm Optimization with Velocity Mutation

Zaharis, Zaharias D., Gravas, Ioannis P., Yioultsis, Traianos V., Lazaridis, Pavlos I., Glover, Ian A., Skeberis, Christos and Xenos, Thomas D. (2017) Exponential Log-Periodic Antenna Design Using Improved Particle Swarm Optimization with Velocity Mutation. IEEE Transactions on Magnetics, 53 (6). ISSN 0018-9464

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Abstract

An improved particle swarm optimization (PSO) method applied to the design of a new wideband log-periodic antenna (LPA) geometry is introduced. This new PSO variant, called PSO with velocity mutation (PSOvm), induces mutation on the velocities of those particles that cannot improve their position. The proposed LPA consists of wire dipoles with lengths and distances varied according to an exponential rule, which is defined by two specific parameters called length factor and spacing factor. The LPA is optimized for operation in 790-6000MHz frequency range, in order to cover the most usual wireless services in practice, and also to provide in this range the highest possible forward gain, gain flatness below 2dB, secondary lobe level below –20dB with respect to the main lobe peak, and standing wave ratio below 2. To demonstrate its superiority in terms of performance, PSOvm is compared to well-known optimization methods. The comparison is performed by applying all the methods on several test functions and also on the LPA optimization problem defined by the above-mentioned requirements. Furthermore, the radiation characteristics of the PSOvm-based LPA give prominence to the effectiveness of the proposed exponential geometry compared to the traditional Carrel’s geometry.

Item Type: Article
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Z Bibliography. Library Science. Information Resources > Z719 Libraries (General)
Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases
Schools: School of Computing and Engineering
Related URLs:
Depositing User: Pavlos Lazaridis
Date Deposited: 23 Mar 2017 13:26
Last Modified: 09 Nov 2017 08:51
URI: http://eprints.hud.ac.uk/id/eprint/31618

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