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Efficient global optimization method via clustering/classification methods and exploration strategy

机译：通过聚类/分类方法和探索策略高效的全局优化方法

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摘要

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.
机译：本研究的目的是通过使用机器学习技术有效地解决复杂的高维优化问题。最近，主要优化目标已被改为更复杂的目标，例如不连续和高维优化问题。有必要解决高维优化问题，以从具有大量设计变量的拓扑设计优化获取创新设计，以表达各种拓扑/形状。因此，在本研究中，开发了通过聚类/分类方法和探索策略（EGOCCS）的有效的全局优化方法，以有效地解决高尺寸优化问题而不使用概率值作为标准偏差，这通常在高斯过程中被提供/使用/使用，并降低响应面模型的施工成本。解决了两个优化问题以验证EGOCCS开发方法的有用性。执行第一优化以演示由贝叶斯优化的2,10,40,80和160维分析函数问题中的EGOCCS的有效性以进行比较目的。确认具有径向基函数插值方法的EGOCC可以在许多分析功能问题中获得最佳解决方案，具有较大数量的设计变量。执行第二优化以检查EGOCCS在高维空气动力形状优化问题中的效果，用于通过遗传算法进行比较目的解决的二维双凸翼型。确认EGOCC可以在高维空气动力学优化问题中有效地使用。

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