Design and Optimization of Microstrip patch Antenna via Improved Metaheuristic Algorithm

Guttula, Ramakrishna; Nandanavanam, Venkateswara Rao; Satyanarayana, Vella

doi:10.1007/s11277-021-08531-y

Cited by 12 publications

(14 citation statements)

References 40 publications

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“…Here, the analysis was performed based on antenna gain and efficiency. Here, assessment was done overMFO, 2 PSO, 3 PGWAD, 40 MP‐LAD, 33 EHO‐NSFAD, 41 SSO, 34 and SSA 35 . In addition, the investigation was held regarding cost function for varied frequencies, and analysis was done on efficiency, cost, directivity, CI, and gain.…”

Section: Resultsmentioning

confidence: 99%

Hybrid optimization algorithm for optimal designing of microstrip patch antenna

Guttula

Ramesh

Mannepally

2023

Concurrency and Computation

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Microstrip Patch Antennas (MPAs) are generally renowned for their adaptability regarding feasible geometries, which makes them appropriate for numerous diverse conditions. The suitability to integrate and the trivial structure with microwave incorporated circuits was said to be the major advantage among several advantages. MPA poses constricted bandwidth; thus it has a complication while tuning. In addition, MPAs are renowned for their reduced gain. As a result, there is a necessity to raise the gain and bandwidth of MPA. This work intends to put forward a novel approach that gets a non-linear objective for assisting the modeling of solution spaces for antenna constraints. Thus, "Salp Swarm based Shark Smell Optimization (SS-SSO) that hybrids the concepts of Salp Swarm Algorithm (SSA) and Shark Smell Optimization (SSO)" is developed that tuned the constraints of MPA. The implication of the developed approach is to boost the antenna gain by optimal electing of dielectric value, patch length, substrate width, and thickness of MPA.

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Section: Resultsmentioning

confidence: 99%

Hybrid optimization algorithm for optimal designing of microstrip patch antenna

Guttula

Ramesh

Mannepally

2023

Concurrency and Computation

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“…Elephant Herding Optimization (EHO) technique with a novel scaling factor is suggested by [8] to finetune the MPA parameters. To boost the antenna's gain, they tuned factors such substrate thickness, dielectric value, patch length, and patch breadth.…”

Section: Related Workmentioning

confidence: 99%

“…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 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 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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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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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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Design and Optimization of Microstrip patch Antenna via Improved Metaheuristic Algorithm

Cited by 12 publications

References 40 publications

Hybrid optimization algorithm for optimal designing of microstrip patch antenna

Hybrid optimization algorithm for optimal designing of microstrip patch antenna

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

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