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首页> 外文会议>International conference on the physics of reactors;PHYSOR 2010 >LOADING PATTERN OPTIMIZATION COOPERATIVELY USING TWO NEW ALGORITHMS
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LOADING PATTERN OPTIMIZATION COOPERATIVELY USING TWO NEW ALGORITHMS

机译：使用两个新算法协同加载模式优化

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Loading pattern optimization (LPO) for a PWR in nuclear power plant contains three parts: fuel assembly location optimization, burnable poison placement optimization, and used fuel assembly orientation optimization. To solve the former two parts, this paper devises an innovative stochastic evolutionary algorithm—Interval Bound Algorithm (IBA), which can optimize fuel assembly location and burnable poison placement together. IBA just uses the fuel assembly's infinite multiplication factor to get rid of unfavorable patterns and to explore new promising solution space. To solve the last part, this paper applies Estimation of Distribution Algorithms (EDAs), which also belong to evolutionary algorithms. These three parts depend on each other, so it is better not to solve them separately. In order to optimize these parts in a coupled way, we use Symbiotic Co-evolutionary Algorithm (SCA) to incorporate IBA and EDAs. This technique could reflect the real optimization process. Based on these algorithms, the corresponding LPO code of IBALPO is developed. To avoid search direction to offset for inconsistency between the LP search code and the design code, IBALPO directly adopts production core design code to evaluate LPs in a parallel computation environment. Finally, this code system is used to solve a realistic reload problem to show its performance. Obtained results have illustrated that IBALPO is efficient and robust. It can find satisfying LPs in two days using 18 CPUs after evaluating about 10000 LPs for a core containing 157 assemblies.
机译：核电站压水堆的装载模式优化（LPO）包含三个部分：燃料组件位置优化，可燃毒物放置优化和二手燃料组件方向优化。为了解决前两个部分，本文设计了一种创新的随机进化算法-区间约束算法（IBA），该算法可以同时优化燃料组件的位置和可燃毒物的放置。 IBA只是使用燃料组件的无穷倍数来消除不利的模式，并探索新的有前途的解决方案空间。为了解决最后一部分，本文应用了分布算法估计（EDA），它也属于进化算法。这三个部分相互依赖，所以最好不要单独解决它们。为了以耦合的方式优化这些部分，我们使用共生协同进化算法（SCA）合并了IBA和EDA。该技术可以反映实际的优化过程。基于这些算法，开发了相应的IBALPO LPO代码。为了避免搜索方向偏移LP搜索代码和设计代码之间的不一致，IBALPO直接采用生产核心设计代码来评估并行计算环境中的LP。最后，该代码系统用于解决实际的重载问题以显示其性能。所得结果表明，IBALPO是高效且强大的。在为包含157个程序集的内核评估大约10000个LP之后，它可以使用18个CPU在两天内找到满意的LP。

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