PWR core loading pattern optimization with adaptive genetic algorithm

Abstract

In this paper, a new adaptive genetic algorithm (AGA) is proposed for the loading pattern optimization (LPO) of the pressurized water reactor (PWR) core. The tournament selection, the two-point crossover and the mutation based on randomly swapping positions between two fuel assemblies (FAs) are applied. New calculation formulae are introduced to adjust effectively the crossover and mutation probabilities according to the fitness value of the individual. The proposed algorithm is implemented in the LPO for the first core of the 1000 MWe PWR. The objective function is to minimize the maximum radial power peaking factor (RPPF) at the equilibrium of Xe under the constraint condition for the cycle length. The maximum RPPF of the obtained LP is decreased than that of the loading pattern (LP) by the designer. The results show that the proposed AGA is effective to improve the convergence rate of genetic algorithms (GAs) in the LPO.