An optimization method based on combination of cellular automata and simulated annealing for VVER-1000 NPP loading pattern

This paper introduces a design methodology in the context of finding new and innovative design principles by means of optimization techniques. In this method cellular automata (CA) and simulated annealing (SA) were combined and used for solving the optimization problem. This method contains two principles that are neighboring concept from CA and accepting each displacement basis on decreasing of objective function and Boltzman distribution from SA that plays role of transition rule. Proposed method was used for solving fuel management optimization problem in VVER-1000 Russian reactor. Since the fuel management problem contains a huge amount of calculation for finding the best configuration for fuel assemblies in reactor core this method has been introduced for reducing the volume of calculation. In this study reducing of power peaking factor inside the reactor core of Bushehr NPP is considered as the objective function. The proposed optimization method is compared with Hopfield neural network procedure that was used for solving this problem and has been shown that the result, velocity and qualification of new method are comparable with that. Besides, the result is the optimum configuration, which is in agreement with the pattern proposed by the designer.