Sparse array optimization method using a dynamic genetic algorithm

To address the optimization challenges associated with two-dimensional sparse arrays, this study proposes an improved dynamic genetic algorithm.This algorithm focuses on reducing the number of array elements while enhancing peak sidelobe suppression. This proposed algorithm introduces an evolutionary completion index that integrates the number of iterations and population fitness to accurately characterize the evolutionary progress of the population. In addition, according to this index, dynamically adjustable crossover and mutation operators are designed, which enable the algorithm to flexibly adjust crossover and mutation probabilities to satisfy the needs of different optimization stages, thereby improving the overall optimization performance.The experimental results revealed that compared with traditional genetic algorithms, the proposed algorithmcan effectively suppress the peak sidelobe ratio by more than 3.9 dB in sparse array optimization, thereby exhibiting robust adaptability and stability across various sparsity levels.