遗传算法在柱状高温气冷堆换料优化问题中的应用

堆芯换料方案的优化是一个典型的组合优化问题,其搜索空间异常庞大。传统的优化算法很难在如此巨大的搜索空间中寻找出全局最优解。遗传算法以其优良的自适应能力和优化能力,为组合优化问题提供了一个非常有效的解决途径。采用遗传算法对柱状高温气冷堆堆芯装料方案进行了优化,并编写了相应程序。为了提高堆物理的计算精度,堆芯临界计算采用26群输运计算。由于多群输运计算需要大量计算时间,为此对遗传算法进行了并行优化。为了验证遗传算法对柱状高温气冷堆换料的优化能力,构造了一个8组件的小型柱状高温气冷堆换料优化基准题。结果表明,遗传算法在柱状高温气冷堆换料优化问题中具有良好的优化能力和计算稳定性。

Block-type high temperature gas cooled reactor reloading pattern optimization using genetic algorithm

The reactor reloading pattern optimization is a typical combinatorial optimization problem with a huge search space. It is very hard for traditional optimization algorithm to find the global optimal solution in such huge search space. However, for combinatorial optimization problem, the genetic algorithm (GA) provides a very effective solution by its excellent adaptive ability and optimization ability. This paper is focused on the reloading pattern optimization by using GA in a block-type high temperature gas cooled reactor(HTGR) and corresponding programs were written to realize this goal. To improve the calculation accuracy of core physics, the transport calculation with 26 groups is adopted in the core calculation, which will also be time-consuming. To make up for this shortcoming, the parallel optimization of GA is carried out. Finally, a refueling optimization benchmark in a small HTGR is constructed to test the optimization ability of GA. The results show that GA has a good optimization ability and computational stability for reloading pattern optimization in block-type HTGRs.