Novel genetic algorithm for loading pattern optimization based on core physics heuristics

Abstract

A genetic algorithm based on novel genetic operators is implemented for the problem of nuclear fuel loading pattern optimization. This is achieved using rank selection or tournament selection and novel crossover operator and fitness function constructions, e.g., improved crossover and mutation operators by considering the chromosomes as permutations (which is a specific feature of the loading pattern problem) and the “stage fitness function” that separates the different objectives of the optimization. Another novel feature of the algorithm is the consideration of the geometric nature of the problem and the desired loading pattern solutions. A new geometric crossover is developed to utilize this geometric knowledge and its implementation exhibits good results. A comprehensive study is performed on the effect of different adaptive mutation strategies on the performances of the algorithm. The new algorithm is implemented and applied to two benchmark problems and used to study the effect of boundary conditions on the symmetry of the obtained best solutions.