摘要
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.