Improved Sparrow Search Algorithm for Sparse Array Optimization

The synthesis problem of the number of array elements, array element spacing, and array formation is widely concerned in sparse array optimization. The local optimum problem is still an urgent problem to be solved in the existing optimization algorithms. A sparse array optimization algorithm on improved sparrow search algorithm (ISSA) is proposed in this paper. Firstly, a probabilistic following strategy is proposed to optimize the following strategy of the sparrow search algorithm (SSA), and it can improve the global search capability of the algorithm. Secondly, the adaptive local search and Cauchy–Gaussian mutation are used to avoid falling into the local optimum situation, and more high‐quality areas are searched to improve the local extremum escape ability and convergence performance of the algorithm. Finally, the peak sidelobe level (PSLL) is used as the fitness function to adaptively optimize the position of the array elements. Experimental simulations show that the proposed approach has good main lobe response and low sidelobe response. In the sparse planar array, the sidelobe level decreases by −1.41 dB compared with the genetic algorithm (GA) and 0.69 dB lower than the SSA. In sparse linear array, the sidelobe level decreases by −1.09 dB compared with differential evolution algorithm and 0.40 dB lower than the SSA. The optimization of sparse arrays significantly enhances the accuracy and robustness of antenna array error estimation.