Core Loading Pattern Optimization of Thorium Fueled Heavy Water Breeder Reactor Using Genetic Algorithm

摘要

In this work genetic algorithm was proposed to solve fuel loading pattern optimization problem in thorium fueled heavy water reactor. The objective function of optimization was to maximize the conversion ratio and minimize power peaking factor. Those objectives were simultaneously optimized using non-dominated Pareto-based population ranking optimal method. Members of non-dominated population were assigned selection probabilities based on their rankings in a manner similar to Baker's single criterion ranking selection procedure. A selected non-dominated member was bred through simple mutation or one-point crossover process to produce a new member. The genetic algorithm program was developed in FORTRAN 90 while neutronic calculation and analysis was done by COREBN code, a module of core burn-up calculation for SRAC.