Multiobjective optimization: redundant and informative objectives

Authors:
Lino Costa;Pedro Oliveira
Affiliations:
Department of Production and Systems Engineering, School of Engineering, University of Minho, Braga, Portugal;Department of Production and Systems Engineering, School of Engineering, University of Minho, Braga, Portugal
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

In multiobjective optimization there is often the problem of the existence of a large number of objectives. For more than two objectives there is a difficulty with the representation and visualization of the solutions in the objective space. Therefore, it is not clear for the decision maker the tradeoff between the different alternative solutions. Thus, this creates enormous difficulties when choosing a solution from the Pareto-optimal set and constitutes a central question in the process of decision making. Based on a statistical method, Principal Component Analysis, the problem of reduction of the number of objectives is addressed. Several test examples with different number of objectives have been studied in order to evaluate the process of decision making through these methods. Preliminary results indicate that this statistical approach can be a valuable tool on decision making in multiobjective optimization.