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Journal of Optimization
Volume 2013, Article ID 270623, 9 pages
Research Article

Multiobjective Optimization Using Cross-Entropy Approach

1Research Laboratory Electrical Engineering and Automation, University of Médéa, Quartier Ain Dheb, 26000 Medea, Algeria
2Laboratory of Advanced Electronic Systems, University of Médéa, Quartier Ain Dheb, 26000 Medea, Algeria

Received 5 June 2013; Revised 12 September 2013; Accepted 13 September 2013

Academic Editor: Ling Wang

Copyright © 2013 Karim Sebaa et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A new approach for multiobjective optimization is proposed in this paper. The method based on the cross-entropy method for single objective optimization (SO) is adapted to MO optimization by defining an adequate sorting criterion for selecting the best candidates samples. The selection is made by the nondominated sorting concept and crowding distance operator. The effectiveness of the approach is tested on several academic problems (e.g., Schaffer, Fonseca, Fleming, etc.). Its performances are compared with those of other multiobjective algorithms. Simulation results and comparisons based on several performance metrics demonstrate the effectiveness of the proposed method.