Comparison of Real-coding Genetic Algorithm with Particle Swarm Optimization on the bandgap bandwidth maximization problem

In this paper, two global optimization algorithms, particle swarm optimization (PSO) and mean-adaptive real-coding genetic algorithm (MAD-RCGA), are applied to a problem of optimization of non-traditional dielectric electromagnetic bandgap structures (EBG). The problem is formulated in nine dimensions with the goal of finding as large frequency gap between the eight lowest TM bands as possible. Maximizing the frequency gap enables to improve properties of planar patch antenas in wide band.