THE FINE-GRAINED PARALLEL MICRO-GENETIC ALGORITHM AND ITS APPLICATION TO BROADBAND CONICAL CORRUGATED-HORN ANTENNA

The fine-grained parallel micro-genetic algorithm (FGPMGA) is developed to solve antenna design problems. The synthesis of uniformly exited unequally spaced array is presented. Comparison with the micro-genetic algorithm (MGA) has been carried out. It is seen that the FGPMGA significantly outperform...

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Veröffentlicht in:Electromagnetic waves (Cambridge, Mass.) Jg. 138; S. 599 - 611
Hauptverfasser: Chang, Lei, Zhou, Haijing, Chen, Ling-Lu, Xiong, Xiang-Zheng, Liao, Cheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cambridge Electromagnetics Academy 01.01.2013
Schlagworte:
Antennas (Electronics)
Broadband transmission
Genetic algorithms
Properties
Sudan
ISSN:1559-8985, 1070-4698, 1559-8985
Online-Zugang:Volltext
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Zusammenfassung:The fine-grained parallel micro-genetic algorithm (FGPMGA) is developed to solve antenna design problems. The synthesis of uniformly exited unequally spaced array is presented. Comparison with the micro-genetic algorithm (MGA) has been carried out. It is seen that the FGPMGA significantly outperforms MGA, in terms of both the convergence rate and exploration ability. The FGPMGA can also reduce the optimization time. Then the FGPMGA and the body of revolution finite-difference time-domain (BOR-FDTD) are combined to achieve an automated design process for conical corrugated-horn antenna. Numerical simulation results show that the horn antenna has good impedance matching (the VSWR is less than 1.5), stable beamwidth and gain, as well as good rotation symmetry patterns over the whole band 8~13 GHz.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1559-8985
1070-4698
1559-8985
DOI:10.2528/PIER13030908