Adaptive resampling strategy of sequential optimization based on radial basis function surrogate model

作者：Wu Zeping; Wang Donghui; Yang Xixiang; Jiang Zhenyu^*; Zhang Weihua

来源：Journal of National University of Defense Technology, 2014, 36(6): 18-24.

DOI：10.11887/j.cn.201406004

• Convergence performance
• Optimization efficiency
• Radial basis function interpolation
• Radial Basis RBF)
• Resampling
• Sampling point distribution
• Sequential optimization
• Surrogate model

Taking account of that it is difficult to obtain the error function to make sequential re-sampling optimization when the predicted error of sampling points in radial basis RBF) interpolation surrogate model is zero, the constraint of sampling point distribution was applied in the process of sequential re-sampling. Taking advantage of the convergence performance of potential optimal solution, a re-sampling strategy which is suitable for the sequential optimization of RBF interpolation surrogate model was proposed. The strategy matches the input response property of emulation model with the spatial distribution property of sampling points. Simulation results indicate that the optimization efficiency and precision of the proposed strategy is higher than that of the traditional optimization method based on surrogate model. The optimum point can be well predicted and the number of computational times in primitive model can be reduced obviously by the proposed strategy.

• 出版日期2014