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Riccardo E. Zich
Researcher at Polytechnic University of Milan
Publications - 251
Citations - 2068
Riccardo E. Zich is an academic researcher from Polytechnic University of Milan. The author has contributed to research in topics: Evolutionary algorithm & Multi-swarm optimization. The author has an hindex of 23, co-authored 250 publications receiving 1867 citations. Previous affiliations of Riccardo E. Zich include University of Milan & University of Queensland.
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Journal ArticleDOI
Genetical Swarm Optimization: Self-Adaptive Hybrid Evolutionary Algorithm for Electromagnetics
TL;DR: A new effective optimization algorithm suitably developed for electromagnetic applications called genetical swarm optimization is presented and shows itself as a general purpose tool able to effectively adapt itself to different electromagnetic optimization problems.
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Differentiated Meta-PSO Methods for Array Optimization
TL;DR: 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.
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Dispersion relation for bianisotropic materials and its symmetry properties
TL;DR: In this paper, the dispersion relation for an arbitrary general bianisotropic medium is derived in Cartesian coordinates, in a form well suited to imposing the boundary conditions when dealing with layered media with planar and parallel interfaces.
Proceedings ArticleDOI
PSO as an effective learning algorithm for neural network applications
TL;DR: An improved particle swarm optimization (PSO) is introduced as a new tool for training an artificial neural network (ANN) and the typical supervised feed-forward backpropagation algorithm and the classical genetic algorithm are chosen.
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Modified Compact Genetic Algorithm for Thinned Array Synthesis
TL;DR: The Modified compact Genetic Algorithm (M-cGA) is introduced and applied to the synthesis of thinned arrays and outperforms not only the cGA, but also the other optimization schemes previously applied to this kind of problem, both in terms of goodness of the solution and of computational cost.