Multi-objective optimization for antenna design

Poian, Mauro; Poles, Silvia; Bernasconi, F.; Leroux, E.; Steffe, W.; Zolesi, M.

doi:10.1109/comcas.2008.4562817

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…Multi-objective optimization is a useful method for these problems. Hence, a growing number of works were reported in recent years focusing on introducing multi-objective optimization algorithms to microwave engineering [1][2][3][4], covering microwave filters, antennas, microwave passive components and circuits.…”

Section: Introductionmentioning

confidence: 99%

“…In the multi-objective microwave component synthesis area, the research on efficiency improvement is still an open research topic. Most of the available works directly use MOEAs as the search engine and EM simulators as the objective function evaluator [1,2,12]. Hence, the synthesis or optimization process is typically very CPU time expensive.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the synthesis or optimization process is typically very CPU time expensive. Efficiency improvement based on hardware resources has been investigated in [2]. Because the evaluation of different candidate designs in a population is independent from each other in most MOEAs, parallel computation is used.…”

Section: Introductionmentioning

confidence: 99%

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Efficient multi-objective synthesis for microwave components based on computational intelligence techniques

Liu

Aliakbarian

Radiom

et al. 2012

Proceedings of the 49th Annual Design Automation Conference

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Multi-objective synthesis for microwave components (e.g. integrated transformer, antenna) is in high demand. Since the embedded electromagnetic (EM) simulations make these tasks very computationally expensive when using traditional multiobjective synthesis methods, efficiency improvement is very important. However, this research is almost blank. In this paper, a new method, called Gaussian Process assisted multi-objective optimization with generation control (GPMOOG), is proposed. GPMOOG uses MOEA/D-DE as the multi-objective optimizer, and a Gaussian Process surrogate model is constructed ON-LINE to predict the results of expensive EM simulations. To avoid false optima for the on-line surrogate model assisted evolutionary computation, a generation control method is used. GPMOOG is demonstrated by a 60GHz integrated transformer, a 1.6GHz antenna and mathematical benchmark problems. Experiments show that compared to directly using a multi-objective evolutionary algorithm in combination with an EM simulator, which is the best known method in terms of solution quality, comparable results can be obtained by GPMOOG, but at about 1/3-1/4 of the computational effort.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient multi-objective synthesis for microwave components based on computational intelligence techniques

Liu

Aliakbarian

Radiom

et al. 2012

Proceedings of the 49th Annual Design Automation Conference

View full text Add to dashboard Cite

show abstract

“…The topic of multi-objective optimizations is huge because it could be applied in so many applications in various fields including economics, finance, optimal control, process optimization, optimal design, etc. In the field of optimal design alone, diverse applications exist like nano-CMOS voltage-controlled oscillator design [15], antenna design [16], optimal sensor deployment [17], etc., while it hasn't been considered in the design of EMATs. We developed the optimization programs in Matlab, and achieved performance enhancement by decreasing the total number of evaluations of the objective functions.…”

Section: Introductionmentioning

confidence: 99%

A multi-objective structural optimization of an omnidirectional electromagnetic acoustic transducer

et al. 2017

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In this paper an axisymmetric model of an omnidirectional electromagnetic acoustic transducer (EMAT) used to generate Lamb waves in conductive plates is introduced. Based on the EMAT model, the structural parameters of the permanent magnet were used as the design variables while other parameters were fixed. The goal of the optimization was to strengthen the generation of the A0 mode and suppress the generation of the S0 mode. The amplitudes of the displacement components at the observation point of the plate were used for calculation of the objective functions. Three approaches to obtain the amplitudes were discussed. The first approach was solving the peak values of the envelopes of the time waveforms from the time domain simulations. The second approach also involved calculation of the peaks, but the waveforms were from frequency domain model combined with the forward and inverse Fourier transforms. The third approach involved a single frequency in the frequency domain model. Single and multi-objective optimizations were attempted, implemented with the genetic algorithms. In the single objective optimizations, the goal was decreasing the ratio of the amplitudes of the S0 and A0 modes, while in the multi-objective optimizations, an extra goal was strengthening the A0 mode directly. The Pareto front from the multi-objective optimizations was compared with the estimation from the data on the discrete grid of the design variables. From the analysis of the results, it could be concluded that for a linearized steel plate with a thickness of 10mm and testing frequency of 50kHz, the point with minimum S0/A0 could be selected, thus the multi-objective optimization effectively degenerated to the single objective optimization. While for an aluminum plate with a thickness of 3mm and frequency of 150kHz, without further information it would be difficult to select one particular solution from the Pareto front.

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