PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Genovesi S., Monorchio A., Mittra R., Manara G. A Sub-Boundary Approach for Enhanced Particle Swarm Optimization and its Application to the Design of Artificial Magnetic Conductors. Technical report, 2006.
 
 
Abstract
(English)
The particle swarm algorithm is a newly introduced method for electromagnetic optimization problems that is based on the observation of swarm intelligence and particle behavior. The particles, while refining their knowledge about the best location in the search area, also communicate with each other and share their own experience. This combined and synergic use of information yields a powerful tool for solving problems that require the optimization of a relatively large number of parameters. This paper proposes a novel strategy for the initialization of the agents' position within the multidimensional solution domain. In particular, the domain is initially subdivided into sub-domains where the agents are more uniformly distributed. At a second stage, the sub-boundaries are removed and the best position information of each group is passed to each agent; the agents are therefore allowed to explore the whole search space. This procedure results in being very efficient with a strong improvement of the convergence rate to the optimal solution. A comparison between the performance of this new implementation and that of the basic particle swarm algorithm is presented for several test cases. Finally, this new procedure is successfully applied to the synthesis of Artificial Magnetic Conductors (AMCs).
Subject Particle swarm optimization
Frequency selective surfaces
J.2 Physical sciences and engineering


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