為了讓核反應器繼續臨界運轉，核反應器需在週期末進行新燃料裝填，而燃料佈局最佳化問題 (nuclear reactor reloaded optimization problem, NRROP) 即是在尋求最佳的裝填佈局。一般，在滿足額定功率的條件下，最佳化的目標在於使多週期燃料的使用率最大。但本研究的單週期燃料佈局設計，若追求最佳燃料使用率將使該週期末之燃料佈局內含之能量耗盡，使後續週期之佈局所含能量過低。故本研究不去追求固定燃料束組成之單週期燃料佈局的最佳燃料使用率，而是將燃料佈局最佳化問題定義為設計出符合安全規範且具有一定經濟效益之佈局。
過去數十年有許多演算法被發展並應用於解決單週期燃料佈局的最佳化問題，其中量子演化式演算法 (quantum evolutionary algorithm) 因其量子疊加態的概念易於展現所有可能的狀態而被重視。在過去的一些研究中，皆提到量子演化式演算法對於區域最佳解有極佳的搜尋能力，但是卻會有過早收斂的情形發生。本研究致力於優化量子演化式演算法的效用，並發展具有自調式學習能力之量子演化式演算法。具自調式學習機制之量子演化式演算法模擬人類於真實世界的自調式學習法，並實際解決此多重目標的最佳化問題。結果顯示，我們所提出的優之量子演化式演算法可有效改善過早收斂的情形，而具自調式學習能力之量子演化式演算法則能延續搜尋過程中的成功經驗，協助設計符合要求的燃料佈局。

To operate in the critical state, the nuclear power plant must be reloaded every cycle. Typically, the goal of nuclear reactor reloaded optimization problem is to design multi-cycle loading patterns that maximize the utilization of fuels. For the single-cycle loading pattern design problem we work on in this study, maximizing fuel utilization rate will lead to over burn-up fuel assemblies and leave less energy for following cycles. Thus, we are instead searching for a pattern that fulfills the nominal power output and satisfies the safety constraints.
For decades, many algorithms have been developed for loading pattern design. The quantum evolutionary algorithm (QEA) is the one that is famous for its capability of probabilistically representing all possible solutions. A small required population of individuals and superior search capability characterize this type of algorithm although it is also reported to have the premature convergence problem. In this study, we dedicate to propose approaches to resolve this issue. Furthermore, we also develop a scheme of self-regulated learning that will be used accompanied by QEA. The self-regulated learning is a process that mimics how human beings learn and can be used to solve the multi-objective optimization problem at hand. Results from several experiments illustrate the efficacy and performance of the proposed approach.

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