比值法下基于不同选择策略的遗传算法换料优化比较分析

遗传算法是一种应用于反应堆换料优化问题的经典算法，该算法的一个重要组成部分为选择策略。在目前的文献中，选择策略常直接选轮盘赌选择法或随机竞争选择法，缺乏对不同选择策略的比较与分析。为得到寻优能力最强的选择策略，本研究以钍基柱状高温气冷堆1/6堆芯为例，以比值法构造适应度函数，利用DRAGON程序进行堆芯物理计算，结合精英保留策略，对轮盘赌选择法、随机竞争选择法、均匀排序法、指数排序法和确定式选择法5种选择策略的寻优能力进行了比较分析。分析结果表明，在这5种选择策略中，指数排序法的寻优能力最强，是最适合求解换料优化问题的选择策略。 

Comparative Analysis of Genetic Algorithms Based on Different Selection Strategies for Refueling Optimization in the Ratio Method

The genetic algorithm is one of the classic algorithms applied to the refueling optimization. An important part of this algorithm is the selection strategies. The existing studies often directly adopt the roulette wheel selection and stochastic tournament selection, and are lacking in comparison and analysis of different selection strategies. To obtain the selection strategy with the strongest optimization capability, this study, with the 1/6 core of a thorium-based prismatic high-temperature gas-cooled reactor (HTGR) taken as an example, constructs the fitness function in the ratio method, performs core physics calculation using the DRAGON code, and in conjunction with the elitism strategy, compares the optimization capabilities of the five selection strategies, including the roulette wheel selection, stochastic tournament selection, uniform ranking method, exponential ranking selection and deterministic selection. The study results show that the optimization capability of the exponential ranking selection is superior to the other four strategies, so the exponential ranking selection is most suitable for solving the refueling optimization problems.