中文摘要：

代理模型广泛应用于工程优化领域中。提出一种集成最小化置信下限和信赖域的动态Kriging代理模型优化策略,以提高全局收敛性和优化效率。在该优化策略中,利用Maximin拉丁超立方体试验设计方法选取初始样本点建立Kriging代理模型,令置信下限公式中的平衡常数等于已有样本点间的最小距离,并采用遗传算法对置信下限公式进行优化。根据已知信息更新信赖域,在新的信赖域内选取样本点更新代理模型,直至收敛。将该策略应用于数学测试算例和工字梁设计优化实例中,试验对比结果表明该优化策略不仅可以获得最优解,而且能够显著地提高优化效率。

英文摘要：

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.