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Abstract Subjected to the accuracy of surrogate model and premature convergence sometimes occurs, the solution of classical efficient global optimization (EGO) usually can be improved. Regarding this point, this paper presents a thorough study of the EGO with high accuracy optimal solution. The algorithm is based on the Kriging surrogate model, in which the Kriging believe strategy based Expected Improvement (EI) function is adopted, it can help to lead the optimal solution to local optimum in the late period of iteration. Besides, in order to improving the accuracy, cooperating with quasi-Newton method and Powell method are also incorporated. Several representative numerical examples are selected to test the algorithms above, the result shows that the algorithms in this paper can reach more accurate global optimal solution than classical EGO, with less additional cost. At last, an aerodynamic optimization problem is developed, it shows that the drag coefficient decreased 1.11% further than the former EGO, shows its practicability in an engineering environment.
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Received: 26 April 2022
Published: 29 September 2022
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2022, Vol. 5 
