The Ant-Q algorithm applied to the nuclear reload problem

The nuclear core fuel reload optimization is a NP-complete combinatorial optimization problem where the aim is to find a pattern of fuel assemblies that maximizes burnup or minimizes the power peak factor. For decades this problem was solved using an expertʹs knowledge. From the eighties, however, there have been efforts to automate fuel reload. The first relevant effort used simulated annealing, but more recent efforts have shown the genetic algorithmʹs (GA) efficiency on this problem. Following this trend, our aim is to optimize nuclear fuel reload using Ant-Q, a reinforcement learning algorithm based on the Cellular Computing paradigm. Ant-Qʹs results on the traveling salesmen problem, which is conceptually similar to fuel reload, are better than the GAʹs. Ant-Q was tested on fuel reload by the simulation of the first out-in cycle reload of Biblis, a 193 assembly PWR and preliminary tests were performed for the cycle 7 reload of Angra I PWR. Comparing Ant-Qʹs results with the GAʹs, it can be verified that, even without local heuristics, the former algorithm can be used to solve the nuclear fuel reload problem.