Decision-Making in Complicated Geometrical Problems
Amir Mosavi · International Journal of Computer Applications · 2014
Due to increasing the number of decision-making criteria in today's ever complicated geometrical optimization problems, the traditional multiobjective optimization approaches, whether a priori, a posteriori or interactive's, found to be insufficient and ineffective.In this paper the drawbacks of the current algorithms are reviewed and the urgent need for inserting a learning component in the optimization loop is discussed.In the following the methodology of reactive optimization for evolutionary interactive multiobjective optimization for solving complicated geometrical decisionmaking problems is adopted.The proposed brain-computer optimization follows to the paradigm of learning while optimizing, through the use of online machine learning techniques as an integral part of a self-tuning optimization scheme.At the end the effectiveness of the approach to geometrical problems is emphasized by providing the study case of optimal design problem of curves and surfaces.