Kriging metamodel based statistical global optimization technique

Abstract

Global optimization techniques have gained much attention in the design of industrial products because they can find not only systematically the best design solution within the design space, but also provide engineers with multiple alternatives. However, the considerable computational burden of global optimization has been still a challenging problem. In this paper, statistical global optimization technique using the kriging metamodel is proposed in order to resolve the difficulty. Because the proposed method explores a global optimum by means of kriging metamodel that can evaluate inexpensively and quickly responses, computational cost of global optimization can be remarkably reduced. For mathematical problems with strong nonlinearity, it is shown that the proposed method can search for a global optimum accurately as well as efficiently. Finally, global optimization of bogie frame is provided as a practical example.