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Kriging-based convex subspace single linkage method with path-based clustering technique for approximation-based global optimization

机译：基于Kriging的凸子空间单链接方法与基于路径的聚类技术用于基于近似的全局优化

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摘要

This paper proposes an improved approach of the Kriging-based Convex Subspace Single Linkage Method (KCSSL method), which was reported as one of approximation-based global optimization methods. The KCSSL method consists of a convex subspace clustering procedure and a local optimization procedure. For the clustering procedure, previously, the cell-based clustering technique was employed. However, this approach will involve a huge number of convexity estimations in case of a higher dimensional problem. This will cause a very high computational cost, therefore, a path-based clustering procedure is newly developed. At first, a procedure for the convexity estimation with the Kriging method is introduced. Next, outline and detailed procedure of the proposed path-based clustering technique are explained. Also, the proposed method is applied to solving some approximate optimization problems. From the numerical results, validity and effectiveness of the proposed method are discussed.
机译：本文提出了一种基于Kriging的凸子空间单链接方法（KCSSL方法）的改进方法，该方法据报道是基于逼近的全局优化方法之一。 KCSSL方法由凸子空间聚类过程和局部优化过程组成。对于聚类过程，以前使用基于单元的聚类技术。但是，在存在较高维问题的情况下，此方法将涉及大量的凸度估计。这将导致非常高的计算成本，因此，新开发了基于路径的聚类过程。首先，介绍了使用克里格法进行凸度估计的过程。接下来，说明提出的基于路径的聚类技术的概述和详细过程。此外，该方法还用于解决一些近似优化问题。从数值结果讨论了该方法的有效性和有效性。

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