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PIER PIER B PIER C PIER M PIER Letters
2013-04-11
The Fine-Grained Parallel Micro-Genetic Algorithm and Its Application to Broadband Conical Corrugated-Horn Antenna
By
Lei Chang,

    Dr. Lei Chang

    No. 36 Research Institute of CETC

    China

    Homepage

Haijing Zhou,

    Prof. Haijing Zhou

    Institute of Applied Physics and Computational Mathematics

    China

    Homepage

Ling-Lu Chen,

    Dr. Ling-Lu Chen

    Institute of Electromagnetics

    Southwest Jiaotong University

    China

    Homepage

Xiang-Zheng Xiong,

    Dr. Xiang-Zheng Xiong

    Institute of Electromagnetics

    Southwest Jiaotong University

    China

    Homepage

Cheng Liao

    Dr. Cheng Liao

    Southwest Jiaotong University

    China

    Homepage

Progress In Electromagnetics Research, Vol. 138, 599-611, 2013
doi:10.2528/PIER13030908
Abstract
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.
Citation
Lei Chang, Haijing Zhou, Ling-Lu Chen, Xiang-Zheng Xiong, and Cheng Liao, "The Fine-Grained Parallel Micro-Genetic Algorithm and Its Application to Broadband Conical Corrugated-Horn Antenna," Progress In Electromagnetics Research, Vol. 138, 599-611, 2013.
doi:10.2528/PIER13030908
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