Abstract 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 becausewe better bring the experimental curve closer to Treloar one with the parameters optimized by the genetic algorithm.

中文翻译：

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用遗传算法优化奥格登能量模型

摘要 Ogden 模型是用于超弹性材料行为建模的能量密度。这种能量模型提供了大量需要识别的材料参数。在本文中，我们公开了一种识别这些参数的方法：遗传算法。这种方法与 Beda-Chevalier、最小二乘法、定向编程对象方法、PSA（模式搜索算法）和 LMA（Levenberg-Marquardt）的方法相反，允许快速识别好的参数，这些参数为 Ogden 模型提供了非常好的预测单轴拉伸、双轴拉伸和纯剪切。这种预测被认为更好，因为我们更好地使实验曲线更接近于使用遗传算法优化的参数的 Treloar one。