To find the optimal shielding design for compact accelerator-driven neutron sources (CANS) using multiobjective optimization, we developed a new method called nondominated sorting genetic algorithm-Monte Carlo method (NSGA-MC). NSGA-MC employs NSGA-II to optimize the shielding parameters based on calculations made by the Monte Carlo N-Particle Transport Code (MCNP). A layered shielding configuration with two materials of borated polyethylene (BPE) and lead (Pb) in the order of BPE/Pb/BPE/Pb for RIKEN Accelerator-driven Compact Neutron Source (RANS) was examined using this method, and two objectives were optimized simultaneously: equivalent dose rate and shielding structure weight. As a result, a tradeoff relationship between the objectives was finally obtained in the form of a Pareto front. The optimization results revealed significant improvements compared with the current RANS shielding configurations in terms of both dose and weight. The results indicate that a reduction in shielding weight of about 60% can be obtained by adopting the optimized shielding structure design, without sacrificing shielding performance. The performance of the method was discussed by showing advantages of NSGA-MC over the so-called weight sum method.

中文翻译：

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应用NSGA-II和MCNP对紧凑型加速器驱动中子源进行多目标优化屏蔽设计

为了使用多目标优化找到紧凑的加速器驱动中子源（CANS）的最佳屏蔽设计，我们开发了一种称为非支配排序遗传算法-蒙特卡罗方法（NSGA-MC）的新方法。NSGA-MC使用NSGA-II根据蒙特卡罗N粒子传输代码（MCNP）进行的计算来优化屏蔽参数。使用此方法检查了用于RIKEN加速器驱动的紧凑中子源（RANS）的BPE / Pb / BPE / Pb顺序的硼酸聚乙烯（BPE）和铅（Pb）两种材料的分层屏蔽配置，并确定了两个目标同时优化：等效剂量率和屏蔽结构重量。结果，最终以帕累托前沿的形式获得了目标之间的折衷关系。优化结果表明，与目前的RANS屏蔽配置相比，无论在剂量还是重量上都取得了显着改善。结果表明，采用优化的屏蔽结构设计可在不牺牲屏蔽性能的情况下减少约60％的屏蔽重量。通过显示NSGA-MC优于所谓的权重总和方法的优势，讨论了该方法的性能。