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An enhanced hybrid and adaptive meta-model based global optimization algorithm for engineering optimization problems
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DOI：10.1007/s11431-016-6068-4.png)
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Due to the good balance between high efficiency and accuracy, meta-model based optimization algorithm is an important global optimization category and has been widely applied. To better solve the highly nonlinear and computation intensive engineering optimization problems, an enhanced hybrid and adaptive meta-model based global optimization (E-HAM) is first proposed in this work. Important region update method (IRU) and different sampling size strategies are proposed in the optimization method to enhance the performance. By applying self-moving and scaling strategy, the important region will be updated adaptively according to the search results to improve the resulting precision and convergence rate. Rough sampling strategy and intensive sampling strategy are applied at different stages of the optimization to improve the search efficiently and avoid results prematurely gathering in a small design space. The effectiveness of the new optimization algorithm is verified by comparing to six optimization methods with different variables bench mark optimization problems. The E-HAM optimization method is then applied to optimize the design parameters of the practical negative Poisson's ratio (NPR) crash box in this work. The results indicate that the proposed E-HAM has high accuracy and efficiency in optimizing the computation intensive problems and can be widely used in engineering industry.
Keyword:
global optimization
meta-modeling
important region update method
crash box
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