A Novel Ultra Wideband Antenna Design and Parameter Tuning Using Hybrid Optimization Strategy

Kumar, Pankaj; Shilpi, S.; Kanungo, Abhas; Gupta, Varun; Gupta, Neeraj

doi:10.1007/s11277-021-08942-x

Cited by 14 publications

(11 citation statements)

References 28 publications

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“…The population is initialized as 50 and the total iteration is set as 500. The performance of the proposed approach is compared with other approaches like PSO-SA [15], PSO [6], FA [9], ACO [7] and EHO [8]. Table 1 shows the simulation parameters used for the optimization of antenna.…”

Section: Figure 4 Mpa Designmentioning

confidence: 99%

“…The African buffalo algorithm (ABA) and the ACO were coupled by Kumar. P. et al [7] to improve the antenna's characteristics. They make use of geometrical parameters including patch width, patch length, and dielectric constant to create a novel "A"-shaped UWB antenna.…”

Section: Related Workmentioning

confidence: 99%

“…The performance of the optimally designed antenna with the RSA approach is evaluated in terms of the radiation pattern, return loss, gain, computation time, directivity, VSWR, and Directivity for Ku-band applications. The comparison of results is performed for the proposed RSA approach with existing approaches conventional PSO-SA [16], PSO [6], ACO [7], EHO [8] and FA [9].…”

Section: Introductionmentioning

confidence: 99%

“…These constraints can be mitigated by proper design considerations. To achieve high gain and low return loss, the optimal selection of geometrical factors such as MPA width and length is a crucial design task [5.] In recent years, meta-heuristic optimization algorithms such as Particle Swarm Optimization (PSO) algorithm [6], Ant Colony Optimization (ACO) algorithm [7], Elephant Herding Optimization (EHO) algorithm [8] and Firefly Algorithm (FA) algorithm [9] are proposed by many researchers, for the optimal selection of geometrical parameters of MPA. A very effective algorithm among other optimization algorithms is RSA and it is mostly used for the optimal design of antenna [10].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Design of H-shaped MPA using Reptile Search Algorithm based Multilayer Perceptron Neural Network

Kumar

2022

Preprint

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In many applications such as aerospace systems, satellites, mobile radar, and other wireless applications, the Microstrip patch antenna (MPA) plays an important role due to its properties like lightweight, low cost of production, and compact structure. Low gain, narrow frequency bandwidth, and high return loss are the shortcomings in these existing MPA design approaches. Moreover, the developed models of the antenna are hard to design and larger size. The antenna's geometrical specifications should be optimized to address this problem. This proposed approach Reptile Search Algorithm (RSA) based Multilayer perceptron (MLP) neural network is employed to design the H-shaped antenna for Ku-band applications. The MLP neural network is employed to calculate the fitness value of the RSA. To train the MLP neural network by using MATLAB software. The experimental and simulation results of the proposed approach shows better performance with 8.89 dB gain, -33.06 dB return loss, and 1.07 VSWR.

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Section: Figure 4 Mpa Designmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design of H-shaped MPA using Reptile Search Algorithm based Multilayer Perceptron Neural Network

Kumar

2022

Preprint

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“…The MPAs' length and width are the geometrical parameters; optimal selection of these parameters is the main design challenge to get low return loss and high gain. 3 Many researchers have proposed various meta-heuristic optimization algorithms for the optimal selection of MPAs' geometrical parameters. Firefly Algorithm (FA), 4 Particle Swarm Optimization (PSO) algorithm, 5 Elephant Herding Optimization (EHO) algorithm, 6 Ant Colony Optimization (ACO) algorithm, 3 Flow Direction algorithm (FDA), Genetic Algorithm (GA), and Material Generation Algorithm (MGA) are the mainly utilized meta-heuristic optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

Design and optimization of triangular microstrip patch antenna using extreme learning machine (ELM)‐based improved Crystal Structure Algorithm (CryStAl) for C‐band application

Kumar,

Shaby

2024

Int J Communication

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SummaryThis article introduces Crystal Structure Algorithm (CryStAl), a novel metaheuristic optimization algorithm, for fine‐tuning microstrip patch antennas (MPA) in C‐band applications, which utilizes CryStAl with dielectric constant and resonant frequency inputs to optimize MPA dimensions, addressing limited bandwidth and gain issues. Achieves superior antenna performance with 32.71 dB return loss, 5.09 dB gain, and 1.2 VSWR, demonstrating improved convergence speed, radiation pattern, and suitability for diverse C‐band conditions.

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A Novel Optimized Recurrent Network-Based Automatic System for Speech Emotion Identification

Koppula¹,

Rao²,

Nabi³

et al. 2022

Wireless Pers Commun

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A Novel Ultra Wideband Antenna Design and Parameter Tuning Using Hybrid Optimization Strategy

Cited by 14 publications

References 28 publications

Design of H-shaped MPA using Reptile Search Algorithm based Multilayer Perceptron Neural Network

Design of H-shaped MPA using Reptile Search Algorithm based Multilayer Perceptron Neural Network

Design and optimization of triangular microstrip patch antenna using extreme learning machine (ELM)‐based improved Crystal Structure Algorithm (CryStAl) for C‐band application

A Novel Optimized Recurrent Network-Based Automatic System for Speech Emotion Identification

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