Abstract
Efficient Global Optimization (EGO) method with Kriging model is rapid, stable and effective for a complex black-box function. However, How to get a more global optimal point on the basis of saving some computation has been concerned in simulation-based design optimization. In order to better solve a black-box unconstrained optimization problem, this paper introduces a new EGO method called improved generalized EGO (IGEGO). In this algorithm, generalized expected improvement (GEI: a new infill sampling criterion) which round off Euclidean norm of θ to replace parameter g may better balance global and local search in IGEGO method. Several numerical tests are given to illustrate the applicability, effectiveness and reliability of the proposed methods.
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