雷达科学与技术2026，Vol.24Issue(2)：140-147,154,9.DOI:10.3969/j.issn.1672-2337.2026.02.003
SEEFO算法驱动的多约束稀布阵列优化方法研究
Sparse Array Optimization Under Multiple Constraints Driven by the SEEFO
摘要
Abstract
To address the array antenna optimization problem under multiple constraints,including a minimum el-ement spacing constraint,a radiation pattern optimization method that integrates a Sobol sequence-based electric eel for-aging optimization(EEFO)algorithm with Gaussian perturbation is proposed in this paper.Firstly,the density-weighting method is employed to preprocess the array,thereby improving the efficiency of array optimization.Secondly,based on the density-weighted array,the Sobol sequence is introduced for population initialization,followed by the application of the EEFO algorithm to further optimize the positions of the array elements in search of the global optimum.Finally,to overcome the limitation of the asymmetric matrix mapping method which transforms the problem of solving actual distances into solving two mapping matrices,Gaussian perturbation is applied to the optimized array to ful-ly enhance the array's degrees of freedom.The experimental results confirm that the proposed method significantly re-duces the computational overhead of the optimization process,enhances the degrees of freedom of the array,and achieves effective suppression of the peak sidelobe level.关键词
阵列天线/SEEFO算法/峰值旁瓣电平/高斯扰动/阵元自由度Key words
array antenna/Sobol electric eel foraging optimization(SEEFO)algorithm/peak sidelobe level(PSLL)/Gaussian perturbation/element distribution freedom分类
信息技术与安全科学引用本文复制引用
陈玉锋,龙伟军,何洋洋,徐艺卓,郝振中..SEEFO算法驱动的多约束稀布阵列优化方法研究[J].雷达科学与技术,2026,24(2):140-147,154,9.基金项目
国家自然科学基金(62071440) （62071440）
