  Real-life engineering optimization problems need Multiobjective Optimization
(MOO) tools. These problems are highly nonlinear. As the process of Multiple
Criteria Decision-Making (MCDM) is much expanded most MOO problems in different
disciplines can be classified on the basis of it. Thus MCDM methods have gained
wide popularity in different sciences and applications. Meanwhile the
increasing number of involved components, variables, parameters, constraints
and objectives in the process, has made the process very complicated. However
the new generation of MOO tools has made the optimization process more
automated, but still initializing the process and setting the initial value of
simulation tools and also identifying the effective input variables and
objectives in order to reach the smaller design space are still complicated. In
this situation adding a preprocessing step into the MCDM procedure could make a
huge difference in terms of organizing the input variables according to their
effects on the optimization objectives of the system. The aim of this paper is
to introduce the classification task of data mining as an effective option for
identifying the most effective variables of the MCDM systems. To evaluate the
effectiveness of the proposed method an example has been given for 3D wing
design.
