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首页> 外文期刊>Annals of nuclear energy >Application of Differential Evolution algorithms to multi-objective optimization problems in mixed-oxide fuel assembly design
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Application of Differential Evolution algorithms to multi-objective optimization problems in mixed-oxide fuel assembly design

机译：差分进化算法在混合氧化物燃料组件设计中的多目标优化问题中的应用

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Multi-objective optimization of nuclear engineering fuel assembly design problems is particularly difficult due to the highly non-linear interactions of a large number of possible variables. In addition, effective optimization algorithms are often highly problem-dependent and require extensive tuning, which reduces their applicability to the real world. To address this issue, Differential Evolution (DE) algorithms have been proposed as a new and effective method for heterogeneous fuel assembly optimization design problems. This paper presents the first complete study to investigate their applicability and performance. Firstly, two multi-objective DE algorithms have their performance compared against an Evolutionary Algorithm (EA) from the literature in optimizing a CORAIL mixed-oxide (MOX) fuel assembly for maximum plutonium content and minimum power peaking factor. Statistical analysis of the results shows the DE algorithms exhibit superior performance to the EA. The DE algorithms are then used to optimize a MOX fuel assembly with gadolinia poison, with results showing DE produces assembly designs comparable in performance to those in the literature. Finally, a sensitivity study is conducted on the control parameters of the better performing of the DE algorithms. Results indicate DE performance remains consistent for a wide range of values of both control parameters, suggesting the algorithm is able to perform effectively without requiring user expertise or effort to find the 'optimal' control parameter settings. (C) 2018 Elsevier Ltd. All rights reserved.
机译：由于大量可能变量的高度非线性相互作用，核工程燃料组件设计问题的多目标优化特别困难。此外，有效的优化算法通常高度依赖于问题，并且需要进行大量调整，从而降低了其在现实世界中的适用性。为了解决这个问题，已经提出了差分进化（DE）算法作为解决异构燃料组件优化设计问题的一种新的有效方法。本文提出了第一个完整的研究，以调查其适用性和性能。首先，在优化CORAIL混合氧化物（MOX）燃料组件以获得最大p含量和最小功率峰值因数方面，与文献中的进化算法（EA）相比，两种多目标DE算法具有出色的性能。结果的统计分析表明，DE算法比EA具有更好的性能。然后将DE算法用于优化带有氧化ado中毒的MOX燃料组件，结果表明DE产生的组件设计的性能可与文献中的性能相媲美。最后，对DE算法性能更好的控制参数进行了敏感性研究。结果表明DE性能对于两个控制参数的宽范围值都保持一致，这表明该算法能够有效执行，而无需用户专业知识或精力来寻找“最佳”控制参数设置。 （C）2018 Elsevier Ltd.保留所有权利。

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