A hybrid of genetic algorithm and particle swarm optimization for antenna design

In this paper, a new effective optimization algorithm called PGHA is presented, which combines in the most effective way the properties of two of the most popular evolutionary optimization approaches now in use for the optimization of electromagnetic structures, the Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). Some improved genetic mechanisms based on non-linear ranking selection, competition and selection among several crossover offspring and adaptive change of mutation scaling are adopted in the paper to overcome the drawbacks of standard genetic algorithm. Furthermore, the proposed algorithm is successfully applied to design a linear array with ten elements and a circular array with thirty one elements and obtain the desired beam forms. We try to use a modified Bernstern polynomial to reduce the number of variables when calculating the circular array, and simulation results show the abroad foreground of PGHA in the antenna array design.