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

• DOI: 10.1007/s11042-018-6563-7
• Publication Date: 2018-09-04T19:11:53Z
• Authors: Yaohui Li; Quanyou Zhang; Yizhong Wu; Shuting Wang
• Keywords: Robustness; Benchmark (surveying); Trust region; Global Optimization; Metamodeling

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).