A Wideband Triple U-slot Microstrip Antenna for Indoor Wireless Applications

Padhi, Jyotibhusan; Mohapatra, Madhulita; Dash, Muktikanta

doi:10.1109/icccnt.2018.8493984

Cited by 3 publications

(1 citation statement)

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“…consist of 3 layers, the substrate above the ground plane and a patch etched onto the substrate above it. Recently, three or four band antenna designs with different segments on the patch of a microstrip antenna have attracted the attention of researchers and have been the subject of many studies [2], [5][6][7][8][9]. These designs have a numerous of variable.…”

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confidence: 99%

Chameleon Swarm Algorithm Assisted Optimization of U-Slot Patch Antenna for Quad-Band Applications

Uluslu

2022

IEEE Access

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Existing optimization algorithms are insufficient in the face of problems with difficult measurement functions as well as a large number of design parameters. Therefore, to achieve such challenging applications, the Chameleon swarm algorithm and its variants, which belong to the family of meta-heuristic algorithms that have not been used in any antenna optimization study before, are used together with 3 different objective functions fitted from mathematical models. Then, a U-slot antenna application with 12 different variable design parameters with resonance frequencies of 3.5 GHz, 3.7 GHz, 5.2 GHz and 5.8 GHz is considered as a multidimensional and single-objective optimization problem. In this study, first of all, the success of the algorithm is reinforced by comparing the performance with other commonly used single and multi-objective optimization algorithms. In addition, the results obtained with different population parameters, weight coefficients, objective functions and variant models were compared. All these processes are compared within themselves, and the antenna results of the most successful result are displayed as 3D electromagnetic simulation. The results show that the optimization processes proposed for an antenna designer are a safe, practical and efficient solution for multidimensional and single-objective antenna optimization applications. In addition, any optimization problem with a large number of variable design parameters can undoubtedly be adapted to the Chameleon swarm algorithm.

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confidence: 99%