Abstract
An issue of great interest in nuclear engineering is to optimize the reload of fuel assemblies in the reactor core, which means to find the best configuration of shuffling between the fresh fuel and the remnants ones from previous cycles. Quantum inspired evolutionary algorithms were developed as an alternative to make the conventional evolutionary algorithms more efficient regarding future hardware implementations. This paper presents a new quantum inspired evolutionary algorithm, named Quantum PBIL (QPBIL). It combines the basic concepts of Population-Based Incremental Learning (PBIL) with the concepts of quantum computing as quantum bit and the linear superposition of states used in evolutionary algorithms with quantum inspirations. To prove its effectiveness as an optimization tool, QPBIL was applied to the optimization of cycle 7 of Angra 1, and the results obtained were comparable to those of efficient optimization techniques based on artificial intelligence currently available.
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