With the increase of design space and nonlinearity, the Surrogate-Based Optimization (SBO) process converges more slowly, and shows deficiency in local exploitation. This paper proposes an efficient adaptive global optimization method, of which infill samples are selected within a variable design space. In each refinement cycle, the current design space is divided into several subspaces by a fuzzy clustering algorithm, with respect to the inherent characteristics of samples in the current design space. Thus new infill samples are generated in each of the subspaces by maximizing expected improvement function and minimizing surrogate prediction, and the subspaces are then merged to form a new design space. The proposed method is validated by six analytical tests. In comparison with general SBO method, the proposed method shows better robustness and performance in global exploration and local exploitation, which is suitable for optimization problems with strong nonlinearity and many optima. The application by minimizing drag of RAE2822 airfoil indicates the proposed method performs well in solving engineering problems, and can maintain good efficiency, robustness and adaptability.
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