A NOVEL IGA-EDSPSO HYBRID ALGORITHM FOR THE SYNTHESIS OF SPARSE ARRAYS
A NOVEL IGA-EDSPSO HYBRID ALGORITHM FOR THE SYNTHESIS OF SPARSE ARRAYS
复制标题
DOI：
10.2528/pier08120806
复制
发表时间：
2009
影响因子：
6.7
通讯作者：
Shuai Zhang;S. Gong;Y. Guan;Peng-fei Zhang;Q. Gong
中科院分区：
文献类型：
--
作者：
Shuai Zhang;S. Gong;Y. Guan;Peng-fei Zhang;Q. Gong
在遗传算法和粒子群优化的改进基础上，提出一种IGA-edsPSO(改进遗传算法-极值扰动简单粒子群优化)混合算法。通过减少阵列空间可以提高 GA 的性能。 sPSO（简单PSO）通过丢弃PSO进化方程中的粒子速度向量，可以避免人为确定最大速度向量造成的后期收敛速度慢和精度低的问题。而edsPSO借助极值扰动因子可以更有效地超越局部极值点。所提出的 IGA-edsPSO 混合算法用于具有最小单元间距约束的稀疏阵列的设计。在给定阵列孔径和阵元数量的情况下，通过同步优化HPBW和PSLL，在一定的半功率波束宽度(HPBW)限制下实现了对峰值旁瓣电平(PSLL)的高效抑制。仿真结果表明，与 IGA、标准 PSO、GA-PSO 和 GA-sPSO 相比，使用 IGA-edsPSO 可以获得更快的收敛速度（这意味着更少的计算时间）和更低的旁瓣水平。
Based on the improvements of both Genetic Algorithm and Particle Swarm Optimization, a novel IGA-edsPSO(Improved Genetic Algorithm-extremum disturbed simple Particle Swarm Optimization) Hybrid algorithm is proposed in this paper. An improved performance of GA is achieved by reducing the array space. By discarding the particle velocity vector in the PSO evolutionary equation, the sPSO (simple PSO) can avoid the problem of slow later convergence velocity and low precision caused by determining the maximal velocity vector factitiously. And the edsPSO can overstep local extremum point more effectively with the help of the extremum disturbed factor. The proposed IGA-edsPSO Hybrid algorithm is used in the design of the sparse arrays with minimum element spacing constraint. Given the array aperture and the number of the array elements, the suppression of the peak sidelobe level (PSLL) with a certain half power beamwidth (HPBW) restriction is implemented with a high efficiency by optimizing the HPBW and PSLL synchronously. The simulation results show that faster convergence velocity (which means less computation time) and lower sidelobe level are obtained using IGA-edsPSO compared to IGA, standard PSO, GA-PSO and GA-sPSO.