Antenna Array Pattern Synthesis Based on a Hybrid Particle Swarm Optimization and Genetic Algorithm

This paper introduces a hybrid optimization method, which combines particle swarm optimization and genetic algorithm for antenna array pattern synthesis. The algorithm first removes a quarter of the individuals with a lower fitness value, and copies the remaining individuals with a middle third of the fitness values to form a new population. Then particle swarm optimization and genetic algorithm are carried out, and particle swarm thinking is introduced into mutation operator. This algorithm can better solve the optimization problems of multidimensional and nonlinear problems. The excitation amplitude and phase of the array elements are optimized to achieve the desired low sidelobe level and deep zeros. Our empirical results show that, the proposed method outperforms existing methods according to convergency, efficiency and optimization.