In pressurized water reactors, the fuel reloading problem has
significant meaning in terms of both safety and economics. An optimal loading
pattern is defined as a pattern in which the local power peaking factor
(
$P_q$
) is lower than a predetermined value during one cycle
and the effective multiplication factor (
$k_{eff}$
) is
maximized to extract the maximum energy. This article presents a specialized
genetic algorithm for loading pattern design. The tests on well-researched
cases have shown that the genetic algorithm is capable of finding better
loading patterns than solutions found by direct search methods. However, most
of the previous researchers have considered simple fitness (cost) functions;
therefore the reported solutions cannot minimize the
$P_q$
together with maximization of the
$k_{eff}$
. To solve this difficulty, we used Fuzzy Nonlinear
Programming (FNLP) technique with the genetic algorithm to perform
multi-objective optimization (maximizing the
$k_{eff}$
together
with minimizing the
$P_q$
). The results show that this method
improves the genetic algorithm results compared to previous methods.
