Theoretical study of GDM-SA-SVR algorithm on RAFM steel

With the development of society and the exhaustion of fossil energy, we need to identify new alternative energy sources. Nuclear energy is an ideal choice, but the key to the successful application of nuclear technology is determined primarily by the behavior of nuclear materials in reactors. Therefore, we studied the radiation performance of the fusion material RAFM steel. We used the GDM algorithm to upgrade the annealing stabilization process of simulated annealing algorithm. The yield stress performance of RAFM steel was successfully predicted by the hybrid model, which combined simulated annealing with the support vector machine for the first time. The prediction process was as follows: first, we used the improved annealing algorithm to optimize the SVR model after training on a training dataset. Next, we established the yield stress prediction model of RAFM steel. By testing the model and conducting sensitivity analysis on the model, we can conclude that, compared with other similar models such as the ANN, linear regression, generalized regression neural network, and random forest, the predictive attribute variables cover all of the variables in the training set. Moreover, the generalization performance of the model on the test set is superior to that of other similar prediction models. Thus, this paper introduces a new method for the study of RAFM steel.