Multiple Criteria Decision-Making Preprocessing Using Data Mining Tools
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.
Keywords: Multiple Criteria Decision-Making,
Multiobjective optimization, preprocessing, Data Mining
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