ABSTRACT This paper aims to propose a new methodology for optimizing fuel loading pattern in a nuclear reactor which is important for its higher safety and economic efficiency. Previous researches have proposed various methodologies to decide better loading patterns automatically. However, the processes still require manual operations of engineers to automatically design actual loading patterns. Swarm intelligence algorithm has currently gained interest as a solution to seek the patterns. Although these methodologies generate better patterns, they sometimes struggle with getting out from local optima and fails to complete the optimization. Large and multimodal solution space sometimes captures worse solutions due to local optima. The conventional methodologies struggle with setting proper parameters to get out from local optima. This research focuses on Multi-Swarm Moth Flame Optimization with Predator (MSMFO-P), an improved Moth Flame Optimization (MFO) by applying the concepts of predator and multi-swarm, as new methodologies. The method of MSMFO-P was applied to solve a loading pattern problem and compared with the conventional optimization methods such as simulated annealing (SA), Hybrid genetic algorithm (GA), and particle swarm optimization (PSO). The results of our experimental works indicated that MSMO-P generates better loading patterns than the conventional methodologies.

中文翻译：

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使用带有捕食者的多群蛾火焰优化方法对压水堆进行加载模式优化

摘要 本文旨在提出一种优化核反应堆燃料装载模式的新方法，这对于提高核反应堆的安全性和经济效率非常重要。以前的研究提出了各种方法来自动确定更好的加载模式。然而，这些过程仍然需要工程师的手动操作来自动设计实际的加载模式。群智能算法目前作为寻找模式的解决方案引起了人们的兴趣。尽管这些方法产生了更好的模式，但它们有时难以摆脱局部最优并且无法完成优化。由于局部最优，大型和多模式解决方案空间有时会捕获更差的解决方案。传统方法难以设置适当的参数以摆脱局部最优。本研究的重点是多群飞蛾火焰优化与捕食者 (MSMFO-P)，一种改进的飞蛾火焰优化 (MFO)，通过应用捕食者和多群的概念作为新方法。将MSMFO-P方法应用于求解加载模式问题，并与模拟退火（SA）、混合遗传算法（GA）和粒子群优化（PSO）等传统优化方法进行了比较。我们的实验工作结果表明，MSMO-P 产生比传统方法更好的加载模式。将MSMFO-P方法应用于求解加载模式问题，并与模拟退火（SA）、混合遗传算法（GA）和粒子群优化（PSO）等传统优化方法进行了比较。我们的实验工作结果表明，MSMO-P 比传统方法产生更好的加载模式。将MSMFO-P方法应用于求解加载模式问题，并与模拟退火（SA）、混合遗传算法（GA）和粒子群优化（PSO）等传统优化方法进行了比较。我们的实验工作结果表明，MSMO-P 产生比传统方法更好的加载模式。