This stage involves a decision maker and her/his preferences to find the most preferred solution to the surrogate problem.
The Halme et al. approach introduces the decision-maker's preference in the efficiency analysis, by explicitly locating his most preferred solution vector on the efficient frontier.
Afterwards, the most preferred solution among all Pareto solutions is selected utilizing a fuzzy satisfying method.
After deriving the Pareto set, the most preferred solution is determined using a fuzzy satisfying method.
Recently, the authors focus on combining by Multi-Criteria Decision Analysis and multiobjective evolutionary algorithms to select the most preferred solution from the generated set [5, 6].
In that, a given problem is simplified by identification and elimination of the redundant objectives, before interactively engaging the decision maker to converge to the most preferred solution on the Pareto-optimal front.
The same authors highlight that when systematically exploring the neighborhoods of the Most Preferred Solution (MPS), one does not know explicitly the decision-maker's value function, but its form becomes known when the end of the search for MPS is reached.
An interactive, aspiration-based search process that iteratively progresses toward the most preferred solution can alleviate this task.
They could first tighten the bounds rather cautiously, and converge to their most preferred solution only at the end.
The bend angle related to the trade-off relationship of objective functions is used to select the most preferred model among the solutions on the Pareto optimal front.
