Comparative study of meta-heuristic algorithms for reactor fuel reloading optimization based on the developed BP-ANN calculation method.

• A new BP-ANN calculation method for reactor fuel reloading optimization was developed. • The BP-ANNs developed in this paper have higher precision than those in similar paper. • Five different meta-heuristic algorithms were compared when combining with the BP-ANN calculation method for fuel reloading optimization. • The developed BP-ANN calculation method for reactor fuel reloading optimization obviously fasten the calculation time. In order to obtain optimal core configurations, the problems of fuel reloading optimization must be solved in nuclear engineering field. This problem is a combinational optimization problem with an enormous solution space. In the traditional calculation method, the meta-heuristic algorithms (MHAs) are used to search and update optimization solutions continuously, while the core physics calculation programs are used to calculate the optimization parameters. In this research, we developed a new BP-ANN calculation method to solve the problems of fuel reloading optimization for thorium-based block-type high temperature gas-cooled reactors (HTGRs). In this method, the function of core physics calculation programs are replaced by BP-ANNs in order to reduce calculation time. Besides, five different MHAs, which are genetic algorithm (GA), particle swarm optimization (PSO), Teaching-learning based optimization algorithm (TLBO), harmony search (HS) and Rank-based ant system (RAS), are used under this new method to solve the problems of fuel reloading optimization, respectively. The results shows that PSO is the best MHA with a real feasible solution ratio of 0.30 and the best fitness function value of 0.455904. Compared to the traditional calculation method, the calculation time of BP-ANN calculation method is reduced to 0.7% of the former. [ABSTRACT FROM AUTHOR]