Memetic Variation Local Search vs. Life-Time Learning in Electrical Impedance Tomography
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
A reconstruction method for electrical impedance tomography using particle swarm optimization
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
Image reconstruction using genetic algorithm in electrical impedance tomography
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Clustering-based particle swarm optimization for electrical impedance imaging
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
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Electrical impedance tomography (EIT) determines the resistivity distribution inside an inhomogeneous object by means of voltage and/or current measurements conducted at the object boundary. A genetic algorithm (GA) approach is proposed for the solution of the EIT inverse problem, in particular for the reconstruction of “static” images. Results of numerical experiments of EIT solved by the GA approach (GA-EIT in the following) are presented and compared to those obtained by other more-established inversion methods, such as the modified Newton-Raphson and the double-constraint method. The GA approach is relatively expensive in terms of computing time and resources, and at present this limits the applicability of GA-EIT to the field of static imaging. However, the continuous and rapid growth of computing resources makes the development of real-time dynamic imaging applications based on GAs conceivable in the near future