A Multi-Objective Hybrid Method for Optimizing the Directivity Pattern of Sparse Conformal Array

A hybrid algorithm combining convex optimization and non-dominated sorting genetic algorithm (NSGA) is proposed in this paper, which can achieve directivity pattern synthesis of the sparse conformal arrays. Firstly, a directivity pattern synthesis model of dual-polarized sparse conformal arrays is e...

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Bibliographic Details
Published in2025 IEEE 14th International Conference on Communications, Circuits and Systems (ICCCAS) pp. 354 - 360
Main Authors Liu, Chao, Zhu, Tongtao
Format Conference Proceeding
LanguageEnglish
Published IEEE 23.05.2025
Subjects
Circuits and systems
Convex functions
directivity
Genetic algorithms
Iterative algorithms
multi-objective optimization
Optimization
Simulation
Sorting
sparse conformal arrays
Vectors
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DOI10.1109/ICCCAS65806.2025.11102247

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Summary:A hybrid algorithm combining convex optimization and non-dominated sorting genetic algorithm (NSGA) is proposed in this paper, which can achieve directivity pattern synthesis of the sparse conformal arrays. Firstly, a directivity pattern synthesis model of dual-polarized sparse conformal arrays is established. Then, the convex optimization is embedded into the NSGA, so that the hybrid algorithm can alternately optimize the array weight vector and working state vector. This algorithm can obtain nondominated solutions that maximize sparsity and directivity through iterative solving, while controlling the sidelobe level (SLL) and cross-polarization level (XPL). Finally, simulation results indicate that the algorithm can effectively synthesize the directivity patterns of sparse conformal arrays.
DOI:10.1109/ICCCAS65806.2025.11102247