The particle swarm optimization (PSO) is a stochastic strategy that has recently found application to electromagnetic optimization problems. It is based on the behaviour of insect swarms and exploits the solution space by taking into account the experience of the single particle as well as that of the entire swarm. This combined and synergic use of information yields a promising tool for solving design problems that require the optimization of a relatively large number of parameters. In this paper, the problem of synthesizing Frequency Selective Surfaces (FSSs) is addressed by using a specifically derived particle swarm optimization procedure, which is able to handle, simultaneously, both real and binary parameters. Representative numerical examples are presented to demonstrate the effectiveness of the method. Finally, the performance of the PSO is compared with that of the genetic algorithm.
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