The nuclear fuel cycle cost can be effectively reduced through fuel reloading optimization. Genetic algorithm (GA) is a classic optimization algorithm that is widely applied in fuel reloading optimization. In the GA, selection is a key operator. However, few studies have compared and analyzed different selection strategies. In this study, 1/6 core of thorium-based block-type high temperature gas-cooled reactor was considered as an example, and ten different selection strategies were compared and analyzed. Five of these strategies were the roulette wheel and proportionate selection, tournament selection, uniform sorting, exponential sorting, and deterministic selection, whereas the other five were the aforementioned selection strategies combined with the truncation selection strategy. These ten different selection strategies were evaluated for single-objective and multi-objective problems. In single-objective optimization problems, the effective neutron multiplication factor was selected as the only optimization objective, whereas in multi-objective optimization problems, the effective neutron multiplication factor and power peak factor were considered as optimization objectives. The results indicated that exponential sorting was the best selection strategy for single-objective optimization problems, whereas hybrid truncation exponential sorting was the best selection strategy for multi-objective optimization problems.

核燃料循环成本可通过燃料重装优化而有效降低。遗传算法（GA）是一种经典的优化算法，已广泛应用于燃料重装优化中。在GA中，选择是关键操作员。但是，很少有研究比较和分析不同的选择策略。本研究以th基块状高温气冷堆的1/6堆芯为例，比较分析了十种不同的选择策略。这些策略中的五个是轮盘赌和比例选择，锦标赛选择，统一排序，指数排序和确定性选择，而其他五个是上述选择策略和截断选择策略的组合。针对单目标和多目标问题评估了这十种不同的选择策略。在单目标优化问题中，有效中子倍增因子被选为唯一的优化目标，而在多目标优化问题中，有效中子倍增因子和功率峰值因子被视为优化目标。结果表明，指数排序是单目标优化问题的最佳选择策略，而混合截断指数排序是多目标优化问题的最佳选择策略。而在多目标优化问题中，有效中子倍增因子和功率峰值因子被视为优化目标。结果表明，指数排序是单目标优化问题的最佳选择策略，而混合截断指数排序是多目标优化问题的最佳选择策略。而在多目标优化问题中，有效中子倍增因子和功率峰值因子被视为优化目标。结果表明，指数排序是单目标优化问题的最佳选择策略，而混合截断指数排序是多目标优化问题的最佳选择策略。