Given the highly complex nature of neutronics and reactor physics, efficient methods of optimizing are necessary to effectively design the core reloading pattern and operate a nuclear reactor. The current popular methods for optimization are Simulated Annealing and the Genetic Algorithm; this paper explores the potential for a new method called Greedy Exhaustive Dual Binary Swaps (GEDBS). The mandatory trade-off in computation is accuracy for speed; GEDBS is an exhaustive search and tends toward longer runtimes. While GEDBS performed acceptably for the criterion administered in this paper (local peaking and k, on a Boiling Water Reactor (BWR) fuel lattice) the exhaustive nature of GEDBS will inevitably lead to combinatorial explosion for the addition of the potential dozens of factors that commercial application mandates. This issue may be resolved with the addition of metaheuristics to reduce the search space for GEDBS, or by an increasing computation.
