A Linear Decreasing Inertia Weight Particle Swarm Optimization Base on a $K$-means Clustering Chaotic Sampling for Antenna Design

Qin, Peng-Fei; Li, Wenhao; Wang, Dong; Huang, Guan‐Long; Fan, Wei; Sim, Chow-Yen-Desmond

doi:10.1109/csrswtc56224.2022.10098311

Cited by 1 publication

(1 citation statement)

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“…Designing antennas can be challenging due to various factors, such as the complexity of their struc-ture and the time-consuming nature of the process. To address these issues, several optimization methods have been developed, including surrogate-based optimization [4], particle swarm optimization [5], and spider monkey optimization [6]. These methods aim to simplify the design process and reduce the time and effort required to achieve optimal antenna performance.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Optimizing Antenna Through Genetic Algorithm-Based Neural Network

Korkmaz

Alibakhshikenari

Kouhalvandi

2023

Acta Marisiensis. Seria Technologica

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In this research paper, the application of Artificial Neural Networks (ANN) for designing antennas is introduced. The primary objective of this optimization technique is to focus on the antenna’s parameters to achieve a desirable S11 value, which is a key metric of an antenna’s performance. To demonstrate the effectiveness of this approach, the optimization method to design three different antennas using the Numerical Analyzer (MATLAB) tool is employed. Through this study, it is tried to provide new insights into the optimization of antenna design using ANN and demonstrate its potential applications in the field of antenna engineering. The results of this research could potentially lead to the development of more efficient and effective antenna systems with improved performance in various applications.

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Section: Introductionmentioning

confidence: 99%