An inexpensive cognitive approach for bi-objective optimization using bliss points and interaction

Abstract

When an optimization problem encompasses multiple objectives, it is usually difficult to define optimality. The decision maker plays an important role when choosing the final single decision. Pareto-based evolutionary multiobjective optimization (EMO) methods are very informative for the decision making process since they provide the decision maker with a set of efficient solutions to choose from. Despite that this set may not be the global efficient set, we show in this paper that this set can still be informative within an interactive session with the decision maker. We use a combination of EMO and single objective optimization methods to guide the decision maker in interactive sessions. © Springer-Verlag 2004.