Many-objective rapid optimization of reactor shielding design based on NSGA - III

Abstract

In this paper, a many-objective optimization method for reactor shielding design coupled with NSGA - III and neural network is proposed. By establishing the many-objective optimization model, using Monte Carlo method to calculate samples to train neural network, and the prediction results of neural network are used as the parameters of fitness function for many-objective optimization. The coupling between neural network and NSGA - III is realized, and the Pareto optimal solution of many-objective optimization of reactor shielding design is obtained. The results show that the method of neural network coupling NSGA - III performs well in solving the many-objective optimization problem, and can be applied to the many-objective optimization engineering design of reactor radiation shielding.