基于混合变异差分进化算法的均匀面阵稀疏优化

差分进化算法(DE)已被广泛应用于解决稀疏面阵优化问题,针对DE 算法早熟、全局搜索能力差、容易陷于局部最优的问题,提出一种混合变异差分进化算法,通过加入概率因子来平衡算法收敛速度与全局搜索能力,以阵列孔径、阵元数量以及阵元间距为约束条件,将算法中的实数编码转化为二进制编码,以方向图平面峰值旁瓣电平之和最低为目标函数,通过优化后得到的阵元分布,得到稀疏优化阵列的三维方向图。仿真结果表明:该方法在满足约束条件的同时,能够避免算法早熟得到较优的目标函数值,概率因子为算法提供了额外的自由度。

Sparse Optimization of Uniform Array Based on Hybrid Mutation Differential Evolution Algorithm

Differential evolution algorithm (DE) has been widely used to solve the problem of sparse array optimization. To solve the problem of premature DE algorithm, poor global search ability and easy to fall into local optimum, a hybrid mutation differential evolution algorithm is proposed, which balances the convergence speed and global search ability by adding probability factors. With the constraints of array aperture, number of elements and spacing of elements, the real coding in the algorithm is transformed into binary coding, and the minimum sum of peak sidelobe level (PSLL) in the pattern plane is taken as the objective function. By optimizing the array element distribution, the three-dimensional pattern of sparsely optimized array is obtained. The simulation results show that this method can not only satisfy the constraints, but also avoid premature algorithm to get better objective function values.