MULTIOBJECTIVE PRESSURIZED-WATER REACTOR RELOAD CORE DESIGN BY NONDOMINATED GENETIC ALGORITHM SEARCH

The design of pressurized water reactor reload cores is not only a for midable optimization problem but also, in many instances, a multiobjec tive problem. A genetic algorithm (GA) designed to perform true multio bjective optimization on such problems is described. Genetic algorithm s simulate natural evolution. They differ from most optimization techn iques by searching from one group of solutions to another, rather than from one solution to another. New solutions are generated by breeding from existing solutions. By selecting better (in a multiobjective sen se) solutions as parents more often, the population can be evolved to reveal the trade-off surface between the competing objectives. An exam ple illustrating the effectiveness of this novel method is presented a nd analyzed. It is found that in solving a reload design problem the a lgorithm evaluates a similar number of loading patterns to other state -of-the-art methods, but in the process reveals much more information about the nature of the problem being solved. The actual computational cost incurred depends on the core simulator used; the GA itself is co de independent.