特征统计算法(CSA)在堆芯装料方案优化中的应用

Characteristic Statistic Algorithm (CSA) for In-Core Loading Pattern Optimization

针对压水堆堆芯装料优化问题的特殊性,采用了一种更适合于全局优化算法——特征统计算法来求解。本文阐述了该算法的搜索原理以及如何将其应用于堆芯装料优化问题;编制了装料优化程序SCYCLE,应用该程序在2个不同类型的实际堆芯模型上进行了计算,并与其它算法的计算结果进行了比较。结果表明,SCYCLE程序在求解装料优化问题方面具有很高的搜索效率和很好的全局性。

To-solve the problem of PWR in-core loading pattern optimization, a more suitable global optimization algorithm, i.e., Characteristic statistic algorithm （CSA）, is used. The searching process of this algorithm and how to apply it to this problem are presented. Loading pattern optimization code SCYCLE is developed. Two different problems on real PWR models are calculated and the results are compared with other algorithms. It is shown that SCYCLE has high efficiency and good global performance on this problem.