摘要：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.
