Genetic Algorithm Based Tikhonov Regularization Method for Displacement Reconstruction
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In this paper, a genetic algorithm based Tikhonov regularization algorithm is proposed for determination of global optimal regularization factor in displacement reconstruction. Optimization mathematic models are built by using the generalized cross-validation (GCV) criterion, L-curve criterion and Engl’s error minimization (EEM) criterion as the objective functions to prevent the regularization factor sinking into the local optimal solution. The validity of the proposed algorithm is demonstrated through a numerical study of the frame structure model. Additionally, the influence of the noise level and the number of sampling points on the optimal regularization factor is analyzed. The results show that the proposed algorithm improves the robustness of the algorithm effectively, and reconstructs the displacement accurately.
Key wordsgenetic algorithm Tikhonov regularization displacement reconstruction inverse problem parameter optimization
CLC numberTN 911.7
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