Dielectric filter optimal design suitable for microwave communications by using multiobjective evolutionary algorithms

Goudos, Sotirios K.; Zaharis, Zaharias D.; Salazar-Lechuga, M.; Lazaridis, Pavlos I.; Gallion, P.

doi:10.1002/mop.22755

Cited by 12 publications

(5 citation statements)

References 51 publications

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“…In comparison with nondominated sorting genetic algorithms-II (NSGA-II) and multiobjective evolutionary algorithms (MOEA), MOPSO produces better results for the same population size and for the same number of generations [12]- [14]. Among different versions of MOPSO, the method proposed by Coello et al [9] that provides better diversity is used in this letter.…”

Section: Array Optimizationmentioning

confidence: 95%

Time-Domain Design of UWB Vivaldi Antenna Array Using Multiobjective Particle Swarm Optimization

Chamaani

Abrishamian

Mirtaheri

2010

Antennas Wirel. Propag. Lett.

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A method to obtain optimum tradeoffs (Pareto front) between sidelobe level (SLL) and beamwidth (BW) in time domain for ultrawideband (UWB) antenna arrays is presented. Multiobjective particle swarm optimization (MOPSO) is used to deal with these two conflicting objectives. In the optimization process, mutual coupling effects between elements are not considered. However, the multiple solutions obtained from MOPSO enable the designer to consider the mutual coupling effect as an additional constraint. Each element of the array is an antipodal Vivaldi antenna. The optimization template is applied for three different time-domain pattern descriptors. In comparison to arrays with the same number of elements reported in literature, the Pareto fronts obtained in this study provide lower BW and SLL. A four-element sample from resultant Pareto fronts was simulated by using CST Microwave Studio software. This array shows a 13.7-17.2 dBi gain over the whole frequency band from 3-11 GHz, 7.2 BW, and 12 dB SLL for energy pattern.Index Terms-Antipodal Vivaldi antenna, array antenna, multiobjective particle swarm optimization (MOPSO), ultrawideband (UWB) antenna.

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Section: Array Optimizationmentioning

confidence: 95%

Time-Domain Design of UWB Vivaldi Antenna Array Using Multiobjective Particle Swarm Optimization

Chamaani

Abrishamian

Mirtaheri

2010

Antennas Wirel. Propag. Lett.

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“…ii. The solution is strictly better than in at least one objective Multi objective GA has been widely used in the last decade to solve electromagnetic engineering problems such as; parameter optimizations of different types of antenna designs [35], [36], antenna array optimization [37]- [41], filter [42] and microwave absorber designs [43]- [47]. Many different algorithms have thus far been proposed in solving multi-objective problems.…”

Section: A Multi Objective Optimizationmentioning

confidence: 99%

A Parasitic Layer-Based Reconfigurable Antenna Design by Multi-Objective Optimization

Yuan

Rodrigo

et al. 2012

IEEE Trans. Antennas Propagat.

111

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A parasitic layer-based multifunctional reconfigurable antenna (MRA) design based on multi-objective genetic algorithm optimization used in conjunction with full-wave EM analysis is presented. The MRA is capable of steering its beam into three different directions simultaneously with polarization reconfigurability having six different modes of operation. The MRA consists of a driven microstrip-fed patch element and a reconfigurable parasitic layer, and is designed to be compatible with IEEE-802.11 WLAN standards (5-6 GHz range). The parasitic layer is placed on top of the driven patch. The upper surface of the parasitic layer has a grid of 5 5 electrically small rectangular-shaped metallic pixels, i.e., reconfigurable parasitic pixel surface. The EM energy from the driven patch element couples to the reconfigurable parasitic pixel surface by mutual coupling. The adjacent pixels are connected/disconnected by means of switching, thereby changing the geometry of pixel surface, which in turn changes the current distribution over the parasitic layer, results in the desired mode of operation in beam direction and polarization. A prototype of the designed MRA has been fabricated on quartz substrate. The results from simulations and measurements agree well indicating 8 dB gain in all modes of operation.Index Terms-Beam steering, full-wave analysis, multi-objective genetic algorithm, reconfigurable antenna.

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“…Multiobjective PSO algorithms include the multiobjective particle swarm optimization (MOPSO) [35] and multiobjective particle swarm optimization with fitness sharing (MOPSO-fs) [36]. MOPSO is utilized in [37] for microwave absorber design while MOPSO-fs is applied to the filter design problem in [38] and to antenna base station design in [39].…”

Section: Introductionmentioning

confidence: 99%

Pareto Optimal Microwave Filter Design Using Multiobjective Differential Evolution

Goudos

Sahalos

2010

IEEE Trans. Antennas Propagat.

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Microwave filters play an important role in modern wireless communications. A novel method for the design of multilayer dielectric and open loop ring resonator (OLRR) filters under constraints is presented. The proposed design method is based on generalized differential evolution (GDE3), which is a multiobjective extension of differential evolution (DE). GDE3 algorithm can be applied for global optimization to any engineering problem with an arbitrary number of objective and constraint functions. GDE3 is compared against other evolutionary multiobjective algorithms like nondominated sorting genetic algorithm-II (NSGA-II), multiobjective particle swarm optimization (MOPSO) and multiobjective particle swarm optimization with fitness sharing (MOPSO-fs) for a number of microwave filter design cases. In the multilayer dielectric filter design case a predefined database of low loss dielectric materials is used. The results indicate the advantages of this approach and the applicability of this design method.Index Terms-Dielectric filters, differential evolution (DE), generalized differential evolution (GDE), microwave filter design, multiobjective optimization (MO), open loop ring resonator (OLRR) filter, Pareto optimization, particle swarm optimization (PSO).

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Dielectric filter optimal design suitable for microwave communications by using multiobjective evolutionary algorithms

Cited by 12 publications

References 51 publications

Time-Domain Design of UWB Vivaldi Antenna Array Using Multiobjective Particle Swarm Optimization

Time-Domain Design of UWB Vivaldi Antenna Array Using Multiobjective Particle Swarm Optimization

A Parasitic Layer-Based Reconfigurable Antenna Design by Multi-Objective Optimization

Pareto Optimal Microwave Filter Design Using Multiobjective Differential Evolution

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