A Comparative Analysis of Evolutionary Algorithms for Synthesis of Scanned Linear Array of Mutually Coupled Parallel Dipole Antennas

Patidar, Hemant; Mahanti, G. K.; Muralidharan, R.

doi:10.1515/eletel-2017-0020

Cited by 2 publications

(4 citation statements)

References 26 publications

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“…In this article, we implement a backtracking algorithm, see Figure 1, that, followed by the L-system, generates the geometry of the SA antennas more quickly and efficiently than other approaches. [29][30][31] First, we draw half of the antenna using our method; then, the other half comes out due to an SA antenna symmetry for a 180 rotation required by our approach. Let us start by producing a mesh and setting an initial point randomly.…”

Section: Sa Antenna Geometry By the Backtracking Algorithm And L-systemmentioning

confidence: 99%

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: Sa Antenna Geometry By the Backtracking Algorithm And L-systemmentioning

confidence: 99%

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 QPSO algorithm is thoroughly explained, because it performs better than other algorithms in several antennarelated scenarios [18,[24][25][26][27][28][29]. QPSO serves better than PSO and its variations on well-known benchmark functions [24,25].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, it outperforms the FA in nonuniformly spaced linear antenna array issues with amplitude excitations and element placements [27]. QPSO outperforms PSO in synthesising nonuniformly spaced linear arrays of unequal length parallel dipole antennas for impedance matching, low SLL, main lobe tilting, and uniform null filling, which is commonly used in broadcasting applications [28]. It also outperforms FA and CS in achieving low side lobe level with impedance matching [29].…”

Section: Introductionmentioning

confidence: 99%

“…QPSO outperforms PSO in synthesising nonuniformly spaced linear arrays of unequal length parallel dipole antennas for impedance matching, low SLL, main lobe tilting, and uniform null filling, which is commonly used in broadcasting applications [28]. It also outperforms FA and CS in achieving low side lobe level with impedance matching [29]. Here, the Matlab-based moments code is used to compute the length and spacing of the antenna components, which are then utilised to generate the radiation pattern using the Matlab-HFSS interface.…”

Section: Introductionmentioning

confidence: 99%

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Small planar antenna array design using length and spacing through Matlab‐HFSS interfacing

Patidar,

Das,

Kar

2024

Int J Communication

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SummaryThis study presents a novel method for reducing return loss and achieving lower side lobe levels in small planar antenna arrays (PAAs). Quantum particle swarm optimisation (QPSO) findings are utilised for antenna design modelling and performance assessment utilising the MATLAB‐based moment's code approach and MATLAB‐HFSS interface. The mutual coupling between antenna components is considered and computed using the method of moments (MoM). Changing the array elements' length and spacing will accomplish the abovementioned objectives. The Matlab‐based moments code calculates the antenna components' length and spacing, and the antennas' length and spacing are used to produce the radiation pattern via the Matlab‐HFSS interface. Four examples of small PAA designs are given to illustrate how well the applied methodology works. The method discussed in this paper is generic and can be employed in the future for different sizes of the array elements and the different antenna array structure designs.

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A Comparative Analysis of Evolutionary Algorithms for Synthesis of Scanned Linear Array of Mutually Coupled Parallel Dipole Antennas

Cited by 2 publications

References 26 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

Small planar antenna array design using length and spacing through Matlab‐HFSS interfacing

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