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.