山东科学2026，Vol.39Issue(4)：79-86,8.DOI:10.3976/j.issn.1002-4026.2025046
基于动态遗传算法的稀疏阵列优化方法研究
Sparse array optimization method using a dynamic genetic algorithm
摘要
Abstract
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.关键词
二维稀疏阵列/遗传算法/进化完成度指数/动态参数/副瓣抑制Key words
two-dimensional sparse array/genetic algorithm/evolutionary completion index/dynamic parameters/sidelobe suppression分类
信息技术与安全科学引用本文复制引用
姚凤薇..基于动态遗传算法的稀疏阵列优化方法研究[J].山东科学,2026,39(4):79-86,8.基金项目
重庆市自然科学基金项目(STB2022NSCQ-M3X1605) （STB2022NSCQ-M3X1605）
上海电子信息职业技术学院高层次科研启动项目(CCC2023027) （CCC2023027）
