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 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.