Abstract: The metamodel model is widely used in engineering optimization. an optimization strategy for dynamic metamodel by integrating minimize lower confidence bound and trust region into Kriging metamodel optimization is proposed, in order to enhance global convergence and optimization efficiency. In this strategy, the initial sampling points are firstly selected by maximin Latin hypercube design method and the Kriging metamodel is constructed. During the optimization process, the equilibrium constant is equal to the minimal Euclidean distance between current sampling points, and then genetic algorithm is employed to optimize current equation of lower confidence bound. Subsequently, the trust region is updated according to the current known information, and the new sampling point in the trust region is added to update the metamodel until the potential optimum is satisfied the convergence conditions. Finally, the optimization strategy is validated by using several numerical benchmark problems and the I-beam design optimization problem. Comparing with other optimization strategies, the proposed optimization strategy can not only obtain the optimal solution, but also improve significantly the optimization efficiency.

Key words: dynamic metamodel, Kriging, minimize lower confidence bound, trust region