Abstract
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.
Highlights
Optimization is a basic tool for several decision making in engineering area
We first describe the cases studies used to compare the CE approach to compute the Pareto front with the method based on CE proposed by [21] and the other meta-heuristic methods such as Nondominated Sorting Genetic Algorithm (NSGA)-II, the Pareto Archived Evolution Strategy (PAES), and Strength Pareto Evolutionary Algorithm (SPEA)
The CE with rare event simulation (RES) approach was implemented in two steps
Summary
Optimization is a basic tool for several decision making in engineering area. In these fields, we have many conflicting objectives to satisfy. It contains the solutions that are optimum from an “overall” standpoint and allows the DM to make an informed decision by seeing a wide range of compromise (trade-off). Several randomized optimization algorithms based on CE method have been proposed in the literature and have been shown to lead to good performances on many optimization problems, often outperforming other randomized algorithms [15] In this decade, CE method has been applied in several engineering applications (see, e.g., [16,17,18]). A novel approach for computing the Pareto optimal front based on rare event simulation, the nondominated sorting ranking, and a crowding operator [9] to select the elitist solutions was proposed. The proposed algorithm is presented, and, at the last, the testing application of the proposed algorithm via very known test problem was done, a comparison with other meta-heuristic methods, and concurrent MOCE approach was done
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