Simultaneous estimations of temperature-dependent thermal conductivity and heat capacity using a time efficient novel strategy based on MEGA–NN

The aim of the presented novel strategy is to find the best values of input parameters, while the objective functions are not explicitly known in terms of input parameters and their values only can be calculated by a time-consuming simulation. In this paper, a hybrid modified elitist genetic algorithm–neural network (MEGA–NN) strategy is proposed for such optimization problems. The good approximation performance of neural network (NN) and the effective and robust evolutionary searching ability of modified elitist genetic algorithm (MEGA) are applied in hybrid sense, where NNs are employed in predicting the objective value, and MEGA is adopted in searching optimal designs based on the predicted fitness values. The proposed strategy (MEGA–NN) is used to estimate the temperature-dependent thermal conductivity and heat capacity using inverse heat transfer method. In order to demonstrate the accuracy and time efficiency of the proposed strategy, the results are compared to those of pre-selected parameters and MEGA. Finally, the results show that proposed MEGA–NN could save a great deal of time depending on the case.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► A time efficient novel strategy based on MEGA and NN for optimization problems has been proposed. ► Neural networks are employed to predict the objective value, and modified elitist genetic algorithm is applied to find the best values. ► Neural networks are employed to predict the objective value, and modified elitist genetic algorithm is applied to find the best values. ► A multi-iteration approach is designed and presented which can solve complex problems at no cost of accuracy in less time. ► The application and performance of the proposed algorithm was shown through an inverse problem in simultaneous estimations of thermal properties.