Abstract
One of the key issues that affect the optimization effect of the efficient global optimization (EGO) algorithm is to determine the infill sampling criterion. Therefore, this paper compares the common efficient parallel infill sampling criterion. In addition, the pseudo-expected improvement (EI) criterion is introduced to minimizing the predicted (MP) criterion and the probability of improvement (PI) criterion, which helps to improve the problem of MP criterion that is easy to fall into local optimum. An adaptive distance function is proposed, which is used to avoid the concentration problem of update points and also improves the global search ability of the infill sampling criterion. Seven test problems were used to evaluate these criteria to verify the effectiveness of these methods. The results show that the pseudo method is also applicable to PI and MP criteria. The DMP and PEI criteria are the most efficient and robust. The actual engineering optimization problems can more directly show the effects of these methods. So these criteria are applied to the inverse design of RAE2822 airfoil. The results show the criterion including the MP has higher optimization efficiency.
Highlights
One of the key issues that affect the optimization effect of the efficient global optimization (EGO) algorithm is to determine the infill sampling criterion
This paper introduces the idea of pseudo-expected improvement (EI) criterion into probability of improvement (PI) criterion and minimizing the predicted (MP) criterion
After the pseudo method is introduced into the MP criterion, the problem of falling into the local optima is improved, the optimization rate is relatively slow
Summary
Optimization problems are often involved in engineering design[1]. The objective function values and constraints are mainly obtained by numerical simulation or experiment. The second updated point can be selected without updating the model This method is easier to implement and can be directly introduced into other criteria. First, we take a one-dimensional function as an example to illustrate whether the multiple update points obtained by parallel methods in one iteration can achieve the effect of multiple iterations with the traditional single infill sampling criteria. It can show the similarities and differences of various parallel infill sampling criteria more intuitively. Comparing the difference in the optimal speed and optimal value of different criteria and updating points, which is beneficial to provide a reference for airfoil optimization
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