Sidelobe Suppression Using Modified Multiple Population Genetic Algorithm

In this paper, a modified multiple population genetic algorithm (MMPGA) is proposed and used for the sidelobe suppression. Being different with the initial MPGA, we introduce the novel crossover method into the MMPGA. The novel crossover method generates more candidate chromosomes by traversing all the possible points. In addition, the proposed MMPGA executes the novel crossover operation on the sub-populations to improve the performance. Compared with the random individuals selection strategy, we select the individual with the highest fitness for the proposed crossover method, and retain the elites with the higher fitness into the new population. Therefore, the proposed method is able to significantly reduce the sidelobe level via optimizing the amplitudes of excitations. Numerical results of 25 elements and 20 elements uniform linear array validate the effectiveness of the proposed MMPGA.