基于改进自适应遗传算法的阵列优化

Array Optimization Based on a Modified Adaptive Genetic Algorithm

提出了一种改进的自适应遗传算法,对约束了阵列孔径、阵元数目和最小阵元间距的非均匀稀布阵列进行优化布阵。该算法采用实值编码,改进了适应度函数,避免了不可行解的产生。同时选取新的选择算子和改进的双重最佳保留策略,对传统自适应遗传算法的交叉、变异概率进行了动态改进。仿真结果表明,该方法能较好地抑制"早熟",增加了获取全局最优解的概率,获得了更低的峰值旁瓣电平。

A Modified Adaptive Genetic Algorithm (MAGA)is presented in this paper to optimize the element position of the non-uniform sparse array with the constraints of aperture size,element numbers and minimum element spacing. In the algorithm,real valued coding and the modified fitness function can avoid the appearance of the infeasible solution during mutation and crossover. The new selection operator and improved dual optimal reserved strategy are used in this paper. The classic adaptive probabilities of crossover and mutation are also modified. The simulation results show that the algorithm can suppress premature,increase the probability of obtaining the global optimal solution and get a lower Peak Side-Lobe Level(PSLL).