基于改进整型遗传算法的稀疏矩形平面阵列优化

为了降低固定稀疏率、固定孔径的稀疏矩形阵列的峰值旁瓣电平，提出一种改进整型遗传算法。该算法在整型遗传算法的基础上，提出了等间隔采样的交叉策略、多点变异策略以及优良基因重组的策略。采取等间隔采样的基因交叉方式，可以有效发挥整型编码的优势，从而提高算法的运行效率；为了提高种群的多样性，防止算法陷入局部最优，采用了多点变异策略；采用优良基因重组技术，加快了算法的收敛速度。仿真结果表明，相比传统的二进制和实数编码，整型编码更为直接高效；与用于稀疏矩形阵列优化的相关算法相比，本文所提算法获得了更优的旁瓣电平，证实了算法的有效性和优越性。

Optimization of sparse rectangular planar array using modified integer genetic algorithm

In order to reduce the peak sidelobe level of sparse rectangular array with fixed sparse ratio and fixed aperture, a modified integer genetic algorithm was proposed. On the basis of the integer genetic algorithm, the crossover strategy of equal interval sampling, multi-point mutation strategy and excellent gene recombination strategy were proposed. The crossover strategy of equal interval sampling can effectively exert the advantages of integer coding, which improves the operation efficiency of the algorithm. In order to improve the diversity of the population and avoid falling into the local optimum, the multi-point mutation strategy was adopted. The excellent gene recombination technology was used to accelerate the convergence speed of the algorithm. Simulation results show that, compared with the traditional binary and real coding, the integer coding is more direct and efficient; compared with the related algorithms for sparse rectangular array optimization, the proposed algorithm obtains the better sidelobe level, which proves the effectiveness and superiority of the algorithm.