Research on fast intelligence multi-objective optimization method of nuclear reactor radiation shielding

摘要

Safety and lightweight optimization are significant in developing high-performance compact nuclear reactors. We proposed the reactor radiation shielding optimization method by coupling genetic algorithm and neural network. The engineering design is a multi-objective and multi-constrained complex optimization problem. Thus, we improved it by non-dominated sorting genetic algorithm (NSGA-II) and mini-batch stochastic gradient descent (MSGD), and proposed an adaptive mutation rate operator to improve the global searching capability. The multi-objective optimization models were constructed to verify the feasibility of the method. The optimization results proved it can find the Pareto-optimal front of reactor shielding designs precisely and its calculation speed is two orders of magnitude faster than the pure Monte Carlo (MC) method. By contrast with MC, the method can reduce the calculation time cost and have similar computational accuracy, which demonstrated the applicability and effectiveness of the method in the reactor shielding design applications. (C) 2020 Elsevier Ltd. All rights reserved.