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

doi:10.1007/s00158-011-0643-x

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

2011-03-31

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DOI：10.1007/s00158-011-0643-x

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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.

Keywords:

Approximation-based global optimization

Path-based clustering

Kriging method

Convexity estimation

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

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