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A new effective optimization algorithm suitably developed for electromagnetic applications called genetical swarm optimization (GSO) is presented. This is a hybrid algorithm developed in order to combine 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 (GAs). The algorithm effectiveness has been tested here with respect to both its "ancestors," GA and PSO, dealing with an electromagnetic application, the optimization of a linear array. The here proposed method shows itself as a general purpose tool able to effectively adapt itself to different electromagnetic optimization problems

The particle swarm optimization (PSO) method has been successfully applied to different electromagnetic optimization problems. Because of the complexity of this kind of problems, the associated cost function is in general computationally expensive. A fast convergence of the optimization algorithm is hence required to attain results in short time. Here few variations over the standard algorithm, referred to as differentiated meta-PSO, aimed to enhance the global search capability, and to improve the algorithm convergence, are introduced. In order to verify their effectiveness the different techniques have been first applied to benchmark test functions and then used for the optimization of a planar array

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The design of a broadband reflectarray exploiting a non-conventional shape radiating element to assure a large gain bandwidth is proposed. The element shape presents sufficient degrees of freedom to compensate for the frequency variation of the differential spatial phase delay even when single-layer printed patches are employed

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