Inverse identification of temperature-dependent thermal conductivity via genetic algorithm with cost function-based rearrangement of genes

A new application of the cost function-based rearrangement of genes (proposed by Liu (2008) [1]) is presented in this paper through the genetic algorithm-based solution of the inverse heat conduction problem of identifying the temperature dependent thermal conductivity of a solid material using transient temperature histories. The inverse problem was defined according to the evaluation of the BICOND thermophysical property measurement method. Through the solution of the inverse problem (using simulated measurements), different approaches of the application of the rearrangement of genes were studied and compared. Application of the rearrangement significantly improved the convergence performance and accuracy of the inverse solution compared to a real-valued genetic algorithm, which was adapted to the problem by the authors. In the algorithm that performed best, the rearrangement was applied in an approach different from Liu’s. The effect of random noise added to the temperature history and the effect of regularization was also studied. With significant improvement in computational efficiency, the proposed algorithm is likely to be very effective in evaluation of real measured temperature histories to determine thermophysical properties.