Application of data mining in multiobjective optimization problems

Abstract

In the most engineering optimization design problems, the value of objective functions is not clearly defined in terms of design variables. Instead it is obtained by some numerical analysis such as FE structural analysis, fluid mechanic analysis, and thermodynamic analysis, etc. Usually, these analyses are considerably time consuming to obtain a value of objective functions. In order to make the number of analyses as few as possible a methodology is presented as a supporting tool for the meta-modeling techniques. Researches in meta-modeling for multiobjective optimization are relatively young and there is still much to do. It is shown that visualizing the problem on the basis of the randomly sampled geometrical data of CAD and CAE simulation results, in addition to utilizing classification tool of data mining could be effective as a supporting system to the available meta-modeling techniques. To evaluate the effectiveness of the proposed method a study case in 3D wing design is given. Along with this example, it is discussed how effective the proposed methodology could be in the practical engineering problems.