Since many aerodynamic optimization problems in the area of aeronautics contain highly nonlinear objectives and multiple local optima, it is still a challenge for most of the traditional optimization methods to find t...Since many aerodynamic optimization problems in the area of aeronautics contain highly nonlinear objectives and multiple local optima, it is still a challenge for most of the traditional optimization methods to find the global optima. In this paper, a new hybrid optimization framework based on Differential Evolution and Invasive Weed Optimization（IWO_DE/Ring） is developed, which combines global and local search to improve the performance, where a Multiple-Output Gaussian Process（MOGP） is used as the surrogate model. We first use several test functions to verify the performance of the IWO_DE/Ring method, and then apply the optimization framework to a supercritical airfoil design problem. The convergence and the robustness of the proposed framework are compared against some other optimization methods. The IWO_DE/Ringbased approach provides much quicker and steadier convergence than the traditional methods.The results show that the stability of the dynamic optimization process is an important indication of the confidence in the obtained optimum, and the proposed optimization framework based on IWO_DE/Ring is a reliable and promising alternative for complex aeronautical optimization problems.展开更多
基金supported by the Aeronautical Science Foundation of China (Nos.20151452021 and 20152752033)the National Natural Science Foundation of China (No.61300159)+1 种基金the Natural Science Foundation of Jiangsu Province of China (No.BK20130808)China Postdoctoral Science Foundation (No.2015M571751)
文摘Since many aerodynamic optimization problems in the area of aeronautics contain highly nonlinear objectives and multiple local optima, it is still a challenge for most of the traditional optimization methods to find the global optima. In this paper, a new hybrid optimization framework based on Differential Evolution and Invasive Weed Optimization（IWO_DE/Ring） is developed, which combines global and local search to improve the performance, where a Multiple-Output Gaussian Process（MOGP） is used as the surrogate model. We first use several test functions to verify the performance of the IWO_DE/Ring method, and then apply the optimization framework to a supercritical airfoil design problem. The convergence and the robustness of the proposed framework are compared against some other optimization methods. The IWO_DE/Ringbased approach provides much quicker and steadier convergence than the traditional methods.The results show that the stability of the dynamic optimization process is an important indication of the confidence in the obtained optimum, and the proposed optimization framework based on IWO_DE/Ring is a reliable and promising alternative for complex aeronautical optimization problems.
