IEEE Antennas and Propagation Society International Symposium (IEEE Cat. No.02CH37313)
DOI: 10.1109/aps.2002.1016316
|View full text |Cite
|
Sign up to set email alerts
|

Parallel genetic-algorithm optimization of a dual-band patch antenna for wireless communications

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
5
0

Publication Types

Select...
5
2

Relationship

0
7

Authors

Journals

citations
Cited by 14 publications
(5 citation statements)
references
References 2 publications
0
5
0
Order By: Relevance
“…Researches in micro‐strip patch antennas have seen resurgence in new configurations and patch designs for dual‐band, multi‐band, wideband, and ultra‐wideband wireless applications. Works in L, S, C, and X microwave bands are saturating, and the focus of researchers has now shifted toward the higher frequency bands (Ku and K bands).…”
Section: Introductionmentioning
confidence: 99%
“…Researches in micro‐strip patch antennas have seen resurgence in new configurations and patch designs for dual‐band, multi‐band, wideband, and ultra‐wideband wireless applications. Works in L, S, C, and X microwave bands are saturating, and the focus of researchers has now shifted toward the higher frequency bands (Ku and K bands).…”
Section: Introductionmentioning
confidence: 99%
“…In 2002, A dual band patch antenna using parallel genetic -algorithm for wireless communication was reported by Villegas et al [45].…”
Section: Antennas Design Analysismentioning
confidence: 99%
“…In the literature of topology optimization-based antenna design, the evolutionary structural optimization method has been extensively used, benefitting from the good extendibility of that method. For examples, dual-band antennas were optimized using the genetic algorithm, boolean particle swarm optimization algorithm or other gradient-independent methods ( Johnson and Rahmat-Samii, 1999;Li et al, 2002;Villegas et al, 2002;Afshinmanesh et al, 2008). These kinds of methods, owing not to use the gradient information, require extensive analysis for the large-scale problems, and the application of them is limited by their expensive computational cost (Sigmund, 2011).…”
Section: Introductionmentioning
confidence: 99%