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This paper proposes metamodel optimization methodology based on multi-level fuzzy-clustering space reduction strategy with Kriging interpolation. The proposed methodology is composed of three levels. In the 1st level, the initial samples need partitioning into several clusters due to design variables by fuzzy-clustering method. Sequentially, only some of(More)
In the present paper, a Kriging-based metamodeling technique is used to minimize the risk of failure in a sheet metal forming process. The Krigingbased models are fitted to data that are obtained for larger experimental areas than the areas used in loworder polynomial regression metamodels. Therefore, computational time and memory requirement can be an(More)
Differential Evolution (DE) is one of the most powerful stochastic real parameter optimizers. An alternative adaptive DE algorithm called Expected Improvement (EI)-High Dimensional Model Representation (HDMR)-DE is suggested. The EI criterion and the Kriging-HDMR are used to adjust scale factor F and crossover constant C r , respectively. Considering the(More)
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