Multiple conflicting points of view, which are often taken into account in real life applications, naturally result in a multiple objective optimization problem (MOP) [848]. In order to find the best compromise solution of a MOP, or a good approximation of it, Multiobjective Optimization (MOO) methods need some preference information from a decision maker. According to when and how the preference information is used in the solution procedure, MOO methods can be classified as either methods with a priori, a posteriori, or progressive (interactive) articulation of preferences [400].