Optimisation of the Ogden model by the genetic algorithme method
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The model of Ogden, is a density of energy used in the modeling of hyperelastic materials behavior. This model of energy presents a high number of material parameters to identify. In this paper, we expose a method of identification of these parameters: Genetic Algorithm. This method contrary to the method of Beda-Chevalier, Least Squares, directed programming object method, PSA (Pattern Search Algorithm) and LMA (Levenberg-Marquardt), allows to identify quickly good parameters which give to the Ogden model a very good prediction in uniaxial tension, biaxial tension and pure shear. This predict...
The model of Ogden, is a density of energy used in the modeling of hyperelastic materials behavior. This model of energy presents a high number of material parameters to identify. In this paper, we expose a method of identification of these parameters: Genetic Algorithm. This method contrary to the method of Beda-Chevalier, Least Squares, directed programming object method, PSA (Pattern Search Algorithm) and LMA (Levenberg-Marquardt), allows to identify quickly good parameters which give to the Ogden model a very good prediction in uniaxial tension, biaxial tension and pure shear. This prediction is considered to be better because we better bring the experimental curve closer to Treloar one with the parameters optimized by the genetic algorithm.
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