Multiobjective Optimization: Redundant and Informative Objectives

• Type: Article in International Conference Proceedings Book
• Date: 2009

Authors

Costa, L (Author) Other; oliveira, p (Author) Other

Conference proceedings International

Title: 2009 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-5

Pages: 2008-2015

IEEE Congress on Evolutionary Computation

Trondheim, NORWAY, MAY 18-21, 2009

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 3 Citations

Other information

Authenticus ID: P-003-SRN

DOI: 10.1109/cec.2009.4983187

Abstract (EN): 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 trade-off 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8