Large Thinned Array Design Based on Multi-objective Cross Entropy Algorithm

To consider multi-objective optimization problem with the number of feed array elements and sidelobe level of large antenna array, multi-objective cross entropy（CE） algorithm is proposed by combining fuzzy c-mean clustering algorithm with traditional cross entropy algorithm, and specific program flo...

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Published inShanghai jiao tong da xue xue bao Vol. 20; no. 4; pp. 437 - 442
Main Author 边莉 边晨源 王书民
Format Journal Article
LanguageEnglish
Published Shanghai Shanghai Jiaotong University Press 01.08.2015
Subjects
Algorithms
Antenna arrays
Architecture
Arrays
Computer Science
Electrical Engineering
Engineering
Entropy
Life Sciences
Materials Science
Noise levels
Optimization
Sidelobes
Swarm intelligence
TN 82
multi-objective optimization
cross entropy (CE) algorithm
particle swarm optimization (PSO) algorithm
thinned array
Online AccessGet full text
ISSN1007-1172
1995-8188
DOI10.1007/s12204-015-1645-4

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Summary:To consider multi-objective optimization problem with the number of feed array elements and sidelobe level of large antenna array, multi-objective cross entropy（CE） algorithm is proposed by combining fuzzy c-mean clustering algorithm with traditional cross entropy algorithm, and specific program flow of the algorithm is given.Using the algorithm, large thinned array（200 elements） given sidelobe level（-10,-19 and-30 d B） problem is solved successfully. Compared with the traditional statistical algorithms, the optimization results of the algorithm validate that the number of feed array elements reduces by 51%, 11% and 6% respectively. In addition, compared with the particle swarm optimization（PSO） algorithm, the number of feed array elements from the algorithm is more similar, but the algorithm is more efficient.
Bibliography:To consider multi-objective optimization problem with the number of feed array elements and sidelobe level of large antenna array, multi-objective cross entropy（CE） algorithm is proposed by combining fuzzy c-mean clustering algorithm with traditional cross entropy algorithm, and specific program flow of the algorithm is given.Using the algorithm, large thinned array（200 elements） given sidelobe level（-10,-19 and-30 d B） problem is solved successfully. Compared with the traditional statistical algorithms, the optimization results of the algorithm validate that the number of feed array elements reduces by 51%, 11% and 6% respectively. In addition, compared with the particle swarm optimization（PSO） algorithm, the number of feed array elements from the algorithm is more similar, but the algorithm is more efficient.
31-1943/U
BIAN Li,BIAN Chen-yuan,WANG Shu-min（1. School of Electronic and Information Engineering, Heilongjiang University of Science and Technology, Harbin 150022, China; 2. Department of Electrical and Computer Engineering, Auburn University, Auburn 36849, USA）
thinned array; multi-objective optimization; cross entropy（CE） algorithm; particle swarm optimization（PSO） algorithm
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ISSN:1007-1172
1995-8188
DOI:10.1007/s12204-015-1645-4