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首页> 外文期刊>Annals of nuclear energy >Modified quantum evolutionary algorithm and self-regulated learning for reactor loading pattern design
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Modified quantum evolutionary algorithm and self-regulated learning for reactor loading pattern design

机译:用于反应堆加载模式设计的改进量子进化算法和自律学习

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Reactor-core loading pattern (LP) design is a task performed during refueling to rearrange the spent and newly refilled fuel assemblies (FAs). To attain sufficient cycle length while complying with safety constraints, LP design is not easy, with its difficulty growing exponentially as the number of FAs increases. Attempts to enable design automation have been made with various metaheuristic optimization algorithms. Among them, quantum-inspired algorithms have received much attention because of their small required population of individuals and superior search capability, although the problem of premature convergence also accompanies their applications in LP design. In this paper, a quantum evolutionary algorithm (QEA)-based automated scheme is proposed for LP design in pressurized water reactors. First, the premature convergence in QEA is relieved by modifying its evolutionary mechanism and the decoding scheme to construct an LP. Next, self-regulated learning is incorporated to acquire information that can expedite better LP search so that more satisfactory LPs can be found. The design capability of the proposed scheme is demonstrated with a reference cycle of the Maanshan nuclear power plant, and the results from several experiments illustrate the efficacy of the proposed approaches. (C) 2018 Elsevier Ltd. All rights reserved.
机译:反应堆堆芯装载模式(LP)设计是在加油期间执行的任务,以重新布置用过的和新装的燃料组件(FA)。为了在遵守安全性约束的同时获得足够的周期长度,LP设计并不容易,其难度随着FA数量的增加呈指数增长。已经尝试了各种元启发式优化算法来实现设计自动化。其中,尽管早早收敛的问题也伴随着它们在LP设计中的应用,但量子启发算法因其所需的个体数量少和出色的搜索能力而备受关注。本文提出了一种基于量子进化算法(QEA)的自动化方案,用于压水反应堆的低压设计。首先,通过修改QEA的进化机制和解码方案以构建LP,可以缓解QEA中的过早收敛。接下来,结合自我调节学习以获取可以加速更好的LP搜索的信息,从而可以找到更令人满意的LP。通过马鞍山核电厂的参考周期证明了该方案的设计能力,并且几次实验的结果证明了所提出方法的有效性。 (C)2018 Elsevier Ltd.保留所有权利。

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