On the application of the microgenetic algorithm to the design of broad-band microwave absorbers comprising frequency-selective surfaces embedded in multilayered dielectric media
By S. Chakravarty, Raj Mittra, N. R. Williams. 2001
140 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Electronic, Optical and Magnetic Materials
View Paper: https://www.semanticscholar.org/paper/63666b2452162a2ac96c711e57f69b182a05fa36
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Kim Hyun-Gyung
- Do-Hyoung Kim
- Hak Sung Kim
- Anuj Y. Modi
- Constantine A. Balanis
- Hussein Nasser Shaman
- Craig R. Birtcher
- Zhongxiang Shen
In Electronic, Optical and Magnetic Materials
- Kim Hyun-Gyung
- Do-Hyoung Kim
- Hak Sung Kim
- Anuj Y. Modi
- Constantine A. Balanis
- Hussein Nasser Shaman
- Craig R. Birtcher
- Zhongxiang Shen
Citations in the corpus
- Multiband single-layer frequency selective surface designed by combination of genetic algorithm and geometry-refinement technique (2004, background): “Recently, the global optimizer genetic algorithm (GA) [1], [2] has been applied to the design of complex multilayered FSSs [3], [4].”
- Novel Design of Ultrabroadband Radar Cross Section Reduction Surfaces Using Artificial Magnetic Conductors (2017, methodology): “For example, in [13] and [14] optimization techniques, such as Micro-Genetic and Ant-Colony optimization algorithms, are employed to synthesize these RAMs.”
- On Designing Jaumann and Circuit Analog Absorbers (CA Absorbers) for Oblique Angle of Incidence (2007, background): “The first of these [5] appeared in June 2001 and with the exception of a single design contains only examples that are optimized for only one polarization.”
- Design optimization and manufacture of hybrid glass/carbon fiber reinforced composite bumper beam for automobile vehicle (2015, methodology): “The μGA has been successfully employed to solve the discrete optimal design problems with accelerated convergence compared to the traditional genetic algorithm (GA) which has the serious limitation of severe computing time [24-28].”