The problems of fuel reloading optimization is a complex multi-objective discrete optimization problem with multiple local optimums. In recent years, great process has been made in solving these problems by using meta-heuristic optimization algorithms. Teaching-learning based optimization algorithm (TLBO) is one kind of novel meta-heuristic optimization algorithms. However, it is seldom used to solve the problems of fuel reloading optimization, because its original purpose is to solve continuous optimization problems. In this paper, a hybrid teaching-learning genetic algorithm (HTLGA) is developed, which could be directly applied to solve the problems of fuel reloading optimization. This hybrid algorithm takes TLBO as main part, combines three operators of genetic algorithms (GA) which are coding, crossover and mutation. The optimization solutions which are represented as students in TLBO are further divided into top students, ordinary students and poor students in HTLGA. The calculation phases “Teacher phase” and “Learner phase” in TLBO are improved into “Teacher phase”, “Discussion phase” and “Self-study phase” in HTLGA. For testing the optimization ability of HTLGA, it is applied to solve the problems of fuel reloading optimization for the 1/6 core of thorium-based block-type HTGRs. The results showed that the developed HTLGA has more powerful optimization ability than TLBO and GA.

换油优化问题是一个复杂的多目标离散优化问题，具有多个局部最优解。近年来，使用元启发式优化算法在解决这些问题方面取得了很大进展。基于教学的优化算法（TLBO）是一种新颖的元启发式优化算法。但很少用于解决燃料换装优化问题，因为它的初衷是解决连续优化问题。在本文中，开发了一种混合教学遗传算法（HTLGA），该算法可以直接应用于解决燃料加注优化问题。该混合算法以TLBO为主体，结合了遗传算法(GA)的编码、交叉和变异三个算子。在TLBO中以学生为代表的优化方案在HTLGA中进一步分为优等生、普通学生和差生。TLBO 中的“教师阶段”和“学习者阶段”的计算阶段改进为 HTLGA 中的“教师阶段”、“讨论阶段”和“自学阶段”。为测试HTLGA的优化能力，将其应用于解决钍基块式高温气冷堆1/6堆芯换料优化问题。结果表明，所开发的 HTLGA 比 TLBO 和 GA 具有更强大的优化能力。为测试HTLGA的优化能力，将其应用于解决钍基块式高温气冷堆1/6堆芯换料优化问题。结果表明，所开发的 HTLGA 比 TLBO 和 GA 具有更强大的优化能力。为测试HTLGA的优化能力，将其应用于解决钍基块式高温气冷堆1/6堆芯换料优化问题。结果表明，所开发的 HTLGA 比 TLBO 和 GA 具有更强大的优化能力。