Particle Swarm Optimization of Frequency Selective Surfaces for the Design of Artificial Magnetic Conductors

The particle swarm optimization (PSO) is a population-based optimization algorithm, inspired by concepts as the swarm intelligence and the learning process of the human cognition. The PSO was originally proposed by Kennedy and Eberhart [1] and it has recently found interesting applications within the electromagnetic community [2-4]. In PSO, each member of the swarm represents a codified solution which traverses a multidimensional space. Each dimension of this space is a parameter of the problem to be optimized. During its excursion in the solution domain, each particle in the swarm looks for the best location and changes its position with time. During the flight, each agent in the swarm is attracted towards two different places related to its own experience and those of the other members.