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The aim of this paper is to present an original and efficient approach for indentifying material parameter in biomechanics. A new method named GAO (Genetic algorithms & Analytical Optimization) addresses the parameter identification problem that is formulated as a non-linear least-squares problem. To evaluate GAO technique, the identification problem of 7 material parameters of a specific biomechanical law is approached by multiple algorithms (genetic algorithms and gradient-based methods). This comparative demonstrates the rapidity and the efficiency of GAO method in parameter estimation. It also explains the behaviour of genetic algorithms, their efficient operators, and the advantages that GAO method brings to genetics algorithms leading to successful parameter identification. [ABSTRACT FROM AUTHOR]

Most of the electromagnetic devices, especially electrical machines, have the disadvantage to be exposed to high vibrations caused by magnetic forces. The aim of this study is to propose a methodology to optimize the cylindrical stators generally used in electrical machines regarding the vibration phenomena. Techniques for vibration reduction require knowledge of the proper frequencies, which depend on mechanical shapes and dimensions as well as material properties such as mass density, Young's modulus and Poisson's ratio. This paper proposes a new approach which is based on the identification of mass density (lamination stacking factor) and Young's modulus in the goal to minimize the vibratory behavior of electrical machines. In this goal, we have used artificial intelligent and finite element method (FEM) analysis to solve the magneto-mechanical inverse problem (IP). In the proposed approach, a Multilayer Perceptron Neural Network (MLPNN) is used as a forward model in order to decrease the FEM time consuming. Thus, a Genetic Algorithm (GA) is used to solve the IP in a reasonable time of running. An example study of an induction machine proves that the developed approach may be applied in both design and identification applications. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The article discusses the generalized form of general algorithm optimisation (GAO) parameter identification method in biomechanics. It presents the solution to a nonlinear least squares problem using the GAO method. It demonstrates the method's efficiency by describing the nonlinear elasticity of the human intervertebral disc relative to the seven material parameters of the law. It concludes that GAO has a good gain of central processing unit (CPU) time compared to genetic algorithm (GA).

Abstract: The main goal of this paper is to provide simple parameter identification process for most of hyperelastic constitutive laws in biomechanics of soft tissues. The advantage of our approach lies on its rapidity and effectiveness, by reducing analytically the number of parameters to identify in the model during the identification procedure. With the use of genetic algorithms, the search for an adequate initial guess point is avoided and the space solution of the objective function is reduced to meet only parameters that cannot be calculated analytically. As an example, we focus on models that predict arterial wall behaviour such as laws based on Fung’s type energy function (Holzapfel, 2006) and (Holzapfel et al., 2000 model) . Our approach is applied on uniaxial extension tests and the results are compared with available data in the literature. [ABSTRACT FROM AUTHOR]