An Improved Optimization for Constrained Sparse Planar Arrays Based on Adaptive Genetic Algorithm

This paper proposes a constrained sparse planar array optimization method based on the Adaptive Genetic Algorithm (AGA). By optimizing the positions of array elements, the method effectively reduces the number of elements while maintaining the main lobe gain and lowering sidelobe levels. Simulation results show that AGA outperforms the Standard Genetic Algorithm (SGA) in terms of convergence speed and global optimization capability, effectively avoiding local optima.