Sparse Conformal Array Synthesis Based on Multiagent Genetic Algorithm

In this paper, multiagent genetic algorithm (MAGA) is firstly applied to tackle the synthesis of conformal sparse array, a constrained multi-objective optimization problem. Moreover, a model considered low peak sidelobe level (PSLL) is given for conformal sparse array synthesis. For the antenna arra...

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Bibliographic Details
Published in2022 IEEE 10th Asia-Pacific Conference on Antennas and Propagation (APCAP) pp. 1 - 2
Main Authors Liu, Ganyu, Zhu, Hailiang, Wang, Kai, Mou, Jinchao, Zheng, Pei, Wei, Gao
Format Conference Proceeding
LanguageEnglish
Published IEEE 04.11.2022
Subjects
Antenna arrays
Computational efficiency
Genetic algorithms
Optimization
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DOI10.1109/APCAP56600.2022.10069223

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Summary:In this paper, multiagent genetic algorithm (MAGA) is firstly applied to tackle the synthesis of conformal sparse array, a constrained multi-objective optimization problem. Moreover, a model considered low peak sidelobe level (PSLL) is given for conformal sparse array synthesis. For the antenna array deployed on a quadric surface, the PSLL can be reduced by obtaining the optimal antenna element arrangement. An example of 256-element array synthesis with a 56% sparse rate proves MAGA as an effective optimization tool for conformal sparse arrays in low computational cost.
DOI:10.1109/APCAP56600.2022.10069223