Novel design of a triple band PIFA antenna by using a binary genetic algorithm

Wakrim, Layla; Yassini, Abdessalam El; Khabba, Asma; Ibnyaich, Saida; Hassani, Moha M’Rabet

doi:10.1007/s10825-021-01676-w

Cited by 11 publications

(3 citation statements)

References 22 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%

“…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. 18,19 For example, such concepts have successfully addressed design issues such as beam shaping and control, sidelobe levels, bandwidth, and many others.…”

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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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