Rapid antenna optimization with restricted sensitivity updates by automated dominant direction identification

Pietrenko‐Dabrowska, Anna; Kozieł, Sławomir

doi:10.1016/j.knosys.2023.110453

Cited by 5 publications

(1 citation statement)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Without incurring additional computing overhead, the unit cell model may generate FSS and Filtenna in many frequency ranges. The authors of [ 33 ] suggested limiting antenna response sensitivity updates to dominant directions in parameter space to hasten antenna tuning. The dominant directions are determined by problem-specific information, or more precisely, how estimated antenna characteristics vary when moving through the one-dimensional affine subspaces encompassed by these directions.…”

Section: Related Workmentioning

confidence: 99%

Improved Dipper-Throated Optimization for Forecasting Metamaterial Design Bandwidth for Engineering Applications

et al. 2023

View full text Add to dashboard Cite

Metamaterials have unique physical properties. They are made of several elements and are structured in repeating patterns at a smaller wavelength than the phenomena they affect. Metamaterials’ exact structure, geometry, size, orientation, and arrangement allow them to manipulate electromagnetic waves by blocking, absorbing, amplifying, or bending them to achieve benefits not possible with ordinary materials. Microwave invisibility cloaks, invisible submarines, revolutionary electronics, microwave components, filters, and antennas with a negative refractive index utilize metamaterials. This paper proposed an improved dipper throated-based ant colony optimization (DTACO) algorithm for forecasting the bandwidth of the metamaterial antenna. The first scenario in the tests covered the feature selection capabilities of the proposed binary DTACO algorithm for the dataset that was being evaluated, and the second scenario illustrated the algorithm’s regression skills. Both scenarios are part of the studies. The state-of-the-art algorithms of DTO, ACO, particle swarm optimization (PSO), grey wolf optimizer (GWO), and whale optimization (WOA) were explored and compared to the DTACO algorithm. The basic multilayer perceptron (MLP) regressor model, the support vector regression (SVR) model, and the random forest (RF) regressor model were contrasted with the optimal ensemble DTACO-based model that was proposed. In order to assess the consistency of the DTACO-based model that was developed, the statistical research made use of Wilcoxon’s rank-sum and ANOVA tests.

show abstract

Section: Related Workmentioning

confidence: 99%