An Improved Dual-Sorting NSGA-II Method for Optimal Radiation Shielding Design

The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is a widely used approach for solving multi-objective radiation shielding optimization problems, but it struggles to distinguish equally ranked solutions near the Pareto front, leading to reduced selection pressure and slower convergence. This study proposes an improved dual-sorting NSGA-II method that incorporates a novel dominance determination strategy and an O-based sorting mechanism to overcome these limitations. By leveraging the concave characteristics of Pareto-optimal solutions, the dual-sorting approach improves solution ranking accuracy and maintains population diversity. A case study on a multilayer shielding design demonstrates that the proposed method converges faster than the classical NSGA-II and achieves over 40% higher optimization efficiency in later evolutionary stages. These findings highlight the practical significance of the improved algorithm and its potential to accelerate radiation shielding optimization for nuclear facilities, offering a promising tool for efficient shielding design under multi-objective constraints.