Optimization of the Test Intervals of a Nuclear Safety System by Genetic Algorithms, Solution Clustering and Fuzzy Preference Assignment
Résumé
In this paper, a procedure is developed for identifying a number of representative solutions manageable for decision-making in a multiobjective optimization problem concerning the test intervals of the components of a safety system of a nuclear power plant. Pareto Front solutions are identified by a genetic algorithm and then clustered by subtractive clustering into ''families''. On the basis of the decision maker's preferences, each family is then synthetically represented by a ''head of the family'' solution. This is done by introducing a scoring system that ranks the solutions with respect to the different objectives: a fuzzy preference assignment is employed to this purpose. Level Diagrams are then used to represent, analyze and interpret the Pareto Fronts reduced to the head-of-the-family solutions.