Comparative analysis of the differential evolution and genetic algorithm applied to the nuclear reactor fuel reloading optimization

In-core fuel management optimization (ICFMO) is a prominent problem in the nuclear engineering field. This is a multi-objective problem with large combinatorial solution space, multiple conflicting, nonlinear objective functions. In this paper, two meta-heuristic approaches, Differential evolution and Genetic algorithms were proposed to solve this problem. The optimal objectives are both the maximization of the length and the minimization of power peaking factor in a fuel cycle. The algorithms were applied to the Dalat nuclear research reactor (DNRR). Comparative analysis between two methods and with the actual work configuration was conducted. The results demonstrate that the performance of both algorithms is satisfactory and the DE is more effective than the GA in the DNRR case. (author)