A metamodel optimization methodology based on multi-level fuzzy clustering space reduction strategy and its applications

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 the clusters are involved in building metamodels locally in the 2nd level. Finally, the best optimized result is collected from all metamodels in the 3rd level. The nonlinear problems with multi-humps as test functions are implemented for proving accuracy and efficiency of proposed method. The practical nonlinear engineering problems are optimized by suggested methodology and satisfied results are also obtained.