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A sequential Kriging method assisted by trust region strategy for proxy cache size optimization of the streaming media video data due to fragment popularity distribution

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Abstract

The Kriging method based on machine learning is an attractive tool. In this work, a sequential Kriging method assisted by trust region strategy (SKM-TRS) is proposed to solve unconstrained black-box problems. In this SKM-TRS, the complex and expensive objective function is approximated by Kriging model. And then, a sub-optimization problem, which is constructed by Kriging and a distance factor, is minimized by the improved trust region strategy to determine next update point during each iteration cycle. The proposed method is verified by ten well-known benchmark optimization problems and a proxy cache size optimization of the streaming media video data due to fragment popularity distribution. The final test results demonstrate the efficiency and robustness of the SKM-TRS in contrast with Efficient Global Optimization (EGO), Trust Region Implementation in Kriging-based optimization with Expected improvement (TRIKE) and an Adaptive Metamodel based Global Optimization algorithm (AMGO).

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Acknowledgements

This work is supported by National Natural Science Foundation of China (No. 51775472, No. 51675197, No. 51575205) and also supported in part by the National Key Technology R&D Program of China (No. 2013ZX04005-011).

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Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, Xuchang University, Xuchang, 461000, China

    Yaohui Li & Quanyou Zhang

  2. National CAD Centre, Huazhong University of Science and Technology, Wuhan, China

    Yizhong Wu & Shuting Wang

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  1. Yaohui Li
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  2. Quanyou Zhang
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  3. Yizhong Wu
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  4. Shuting Wang
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Correspondence to Yaohui Li.

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Li, Y., Zhang, Q., Wu, Y. et al. A sequential Kriging method assisted by trust region strategy for proxy cache size optimization of the streaming media video data due to fragment popularity distribution. Multimed Tools Appl 78, 28737–28756 (2019). https://doi.org/10.1007/s11042-018-6563-7

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