Efficient Global Optimization of a Structural Frame

摘要

Major difficulties in applying optimization to engineering problems include a complete structural analysis for each function evaluation and the occurrence of several local minima. A common approach to tackle these problems is to construct cheap global approximation models of the responses often called metamodels or surrogates. These are based on simulation results obtained for a limited number of designs using global data fitting. In this study a two-stage approach is employed based on the Efficient Global Optimization algorithm, EGO. First parallel simulation runs are used to construct a kriging metamodel. In the second stage the metamodel is used to guide the search for promising designs which are adaptively added to the sample. The original EGO algorithm is modified to exploit parallelism and to include general nonlinear constraints. The modified algorithm is applied to the minimum weight design of a structural frame subjected to stress constraints, known to be multimodal.