Abstract
A metamodeling optimization system for nonlinear problems was developed in this study. Boundaries and best neighbors searching (BBNS) intelligent sampling method and fuzzy based progressive metamodeling for space reduction were integrated and applied for this system. The BBNS scheme generates new samples derived from information of boundaries and the best samples of initial sparse distributed samples. It is easy to obtain better samples and avoid local convergence due to boundary information. In order to construct accuracy metamodel, the fuzzy based progressive metamodeling method was implemented to cluster samples generated by BBNS several patches in optimization domain. Only better sets of them are involved in construction of metamodels in each patch by response surface and kriging method. The nonlinear problems with multi-humps as test functions were used for proving accuracy and efficiency of developed system. The practical nonlinear engineering problems were also successfully optimized by this system.
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