The objective of this research is to efficiently solve complicated high dimensional optimization problems by using machine learning technologies. Recently, major optimization targets have been changed to more complicated ones such as discontinuous and high dimensional optimization problems. It is necessary to solve the high-dimensional optimization problems to obtain an innovate design from topology design optimizations that have enormous numbers of design variables in order to express various topologies/shapes. In this research, therefore, an efficient global optimization method via clustering/classification methods and exploration strategy (EGOCCS) is developed to efficiently solve the high dimensional optimization problems without using probabilistic values as standard deviation, that are generally given/utilized in Gaussian process, and to reduce the construction cost of response surface models. Two optimization problems are solved to verify the usefulness of the developed method of EGOCCS. First optimization is executed to demonstrate the validity of the EGOCCS in 2, 10, 40, 80 and 160-dimensional analytic function problems that are also solved by the Bayesian optimization for comparison purposes. It is confirmed that the EGOCCS with radial basis function interpolation approach can obtain the best solutions in many analytic function problems with larger numbers of design variables. Second optimization is executed to examine the effect of the EGOCCS in high dimensional aerodynamic shape optimization problems for a two-dimensional biconvex airfoil that are also solved by a genetic algorithm for comparison purposes. It is confirmed that the EGOCCS can be efficiently used in the high dimensional aerodynamic shape optimization problems.

这项研究的目的是通过使用机器学习技术来有效解决复杂的高维优化问题。最近，主要的优化目标已更改为更复杂的目标，例如不连续和高维优化问题。必须解决高维优化问题，以便从具有大量设计变量的拓扑设计优化中获得创新设计，以表达各种拓扑/形状。因此，在这项研究中，开发了一种通过聚类/分类方法和探索策略（EGOCCS）的有效全局优化方法，以有效解决高维优化问题，而无需使用概率值作为标准偏差，而这些概率值通常是在高斯过程中给出/利用的，并减少响应面模型的建造成本。解决了两个优化问题，以验证所开发的EGOCCS方法的有效性。首先进行优化以证明EGOCCS在2、10、40、80和160维解析函数问题中的有效性，贝叶斯优化也解决了这些问题，以进行比较。可以肯定的是，采用径向基函数插值方法的EGOCCS可以在许多设计变量较多的解析函数问题中获得最佳解决方案。执行第二次优化以检查EGOCCS在二维双凸面机翼的高维空气动力学形状优化问题中的作用，该问题也通过遗传算法解决，以进行比较。