The optimization of electrical machines and energy-harvesting devices has been one of the hot topics of research in recent years. In this context, advanced computational intelligence algorithms are suitable design tools, since they are able to optimize complex systems with a large number of constraints and free design parameters. This paper presents the social network optimization (SNO): a new population-based algorithm developed to guarantee an effective and faster exploration of the solution domain with respect to traditional optimization techniques. The proposed technique will be described and validated with a benchmark case study. Finally, the optimization problem of a tubular permanent magnet linear generator will be formulated in the context of vehicular energy-harvesting systems.

Design of Tubular Permanent Magnet Generators for Vehicle Energy Harvesting by Means of Social Network Optimization

F. Grimaccia;G. Gruosso;M. Mussetta;A. Niccolai;R. E. Zich
2018

Abstract

The optimization of electrical machines and energy-harvesting devices has been one of the hot topics of research in recent years. In this context, advanced computational intelligence algorithms are suitable design tools, since they are able to optimize complex systems with a large number of constraints and free design parameters. This paper presents the social network optimization (SNO): a new population-based algorithm developed to guarantee an effective and faster exploration of the solution domain with respect to traditional optimization techniques. The proposed technique will be described and validated with a benchmark case study. Finally, the optimization problem of a tubular permanent magnet linear generator will be formulated in the context of vehicular energy-harvesting systems.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1041012
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