Optimization of a Dual-band Microstrip antenna Array using Genetic Algorithms

Zeghdoud, Abdelbaki; Derbal, Mohammed Chérif; Nedil, Mourad

doi:10.1109/apusncursinrsm.2018.8609058

Cited by 2 publications

(4 citation statements)

References 2 publications

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“…The literature reports on many optimization methods to design antennas; the return loss S 11 (see, e.g., previous works 3,7,15,30,[32][33][34][35] ), gain and bandwidth, 36 and the VSWR, 16,18 among others. We choose to minimize the VSWR in this paper.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…To achieve the desired antenna specifications based on well‐known geometries, researchers adjusted these geometries manually since to get the best design has been a state‐of‐art and needs more experience through a trial‐and‐error process. Recently, genetic algorithms (GAs) have been considered to optimize the antenna 7,8 with the required specifications and make the design procedure automatic 9–16 …”

Section: Introductionmentioning

confidence: 99%

“…6 To achieve the desired antenna specifications based on well-known geometries, researchers adjusted these geometries manually since to get the best design has been a state-ofart and needs more experience through a trial-and-error process. Recently, genetic algorithms (GAs) have been considered to optimize the antenna 7,8 with the required specifications and make the design procedure automatic. [9][10][11][12][13][14][15][16] Applying mathematical and geometrical concepts such as fractals, aperiodic tilings, special polynomials, 17 and random elements can provide elegant solutions to complex antenna design problems.…”

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confidence: 99%

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Genetic algorithm for the design of a multiband microstrip antenna with self‐avoiding geometry obtained by backtracking

Pérez‐Moroyoqui

Rodríguez‐Romo

Ibáñez‐Orozco

2022

Int J Communication

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The growth of small communication devices has pushed designers to design compact-size antennas. These antennas must be low cost, lightweight, needed for mechanically robust construction, and ease of installation. In this paper, we use a novel genetic algorithm (GA) to produce an ensemble of compact self-avoiding (SA) antennas such that the voltage standing wave ratio (VSWR) is less than 2 at the desired frequencies (as a measure of efficiency); an optimization criterion to get a desirable multiband antenna in the ultra high frequency (UHF) band. First, the electromagnetic properties of these antennas are simulated using the method of moments in MATLAB. SA antennas form a particular set of dipoles introduced here by applying a novel backtracking algorithm. We also use a Lindenmayer system that generates the geometries by a Turtle graphics render to reach our goal. Next, we simulate the VSWR for 150,000 SA antennas with lengths L from 0.1 to 0.4 m; 10 different frequencies are chosen randomly within the UHF band to obtain our results. All the VSWR ≤ 2 are clustered using density-based spatial clustering of applications with noise algorithm (DBSCAN), so our method provides the frequency ranges where this condition is fulfilled. From this, we obtain the VSWR minimums used for the antenna design. Finally, as an instance, we use our results to select the appropriate size of a multiband antenna and, through genetic algorithms, modify the geometry to satisfy the design requirements.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Genetic algorithm for the design of a multiband microstrip antenna with self‐avoiding geometry obtained by backtracking

Pérez‐Moroyoqui

Rodríguez‐Romo

Ibáñez‐Orozco

2022

Int J Communication

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“…A design of the array element using GA almost focuses on side-lobe level (SLL) reduction, beam broadening, and null steering for adaptive beam forming [19][20][21]. Also optimization of array elements such as a slot, a log periodic element, and a microstrip patch is done by using a GA toolbox offered from HFSS and CST MWS to improve axial ratio, gain, SLL, and ARC [22][23][24]. Generally, the design to optimize the array element by using GA toolbox is considerably restricted for multiple frequencies and beam scan angles.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Wide-Band and Wide Angle Cavity-Backed Microstrip Patch Array Using Genetic Algorithm

Kim¹,

Han²,

Kim³

et al. 2020

PIER M

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This paper specifies optimization of a low active reflection coefficient (ARC) array element with a cavity-backed microstrip patch (CBMP) using a genetic algorithm (GA) at wide-band and 2-dimensional (2D) wide angle. Both the GA implemented with a user-defined MATLAB code and a 3-dimensional (3D) full-wave electromagnetic simulator CST MWS are simulated with a real-time direct link. An optimization method using not a traditional unit cell or a small array but a 15 × 15 finite array structure is proposed to apply to a large-scale array antenna. The CBMP array antenna to meet a design goal of a max ARC is optimally designed at equally divided 9 frequencies and 11374 beam angles for S-band 400 MHz operating frequency bandwidth and beam scan coverage (Az = −60 • ∼ +60 • , El = −3 • ∼ +90 •). Measurement results show that a prototype and a full-scale array antenna have low ARC below −8.1 dB and −6.9 dB, respectively for required wide frequency bandwidth and beam scan coverage. It is confirmed that the proposed method is a good solution for optimizing a large-scale array antenna.

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Optimization of a Dual-band Microstrip antenna Array using Genetic Algorithms

Cited by 2 publications

References 2 publications

Genetic algorithm for the design of a multiband microstrip antenna with self‐avoiding geometry obtained by backtracking

Genetic algorithm for the design of a multiband microstrip antenna with self‐avoiding geometry obtained by backtracking

Optimization of Wide-Band and Wide Angle Cavity-Backed Microstrip Patch Array Using Genetic Algorithm

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