Korean Journal of Computational Design and Engineering (한국CDE학회논문집)

Society for Computational Design and Engineering (한국CDE학회)
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An Efficient Heuristic Algorithm of Surrogate-Based Optimization for Global Optimal Design Problems

전역 최적화 문제의 효율적인 해결을 위한 근사최적화 기법

  • Lee, Se-Jung (Department of Mechanical and Information Engineering, University of Seoul)
  • 이세정 (서울시립대학교 기계정보공학과)
  • Received : 2012.06.08
  • Accepted : 2012.09.06
  • Published : 2012.10.01
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Abstract

Most engineering design problems require analyses or simulations to evaluate objective functions. However, a single simulation can take many hours or even days to finish for many real world problems. As a result, design optimization becomes impossible since they require hundreds or thousands of simulation evaluations. The surrogate-based optimization (SBO) strategy became a remedy for such computationally expensive analyses and simulations. A surrogate-based optimization strategy has been developed in this study in order to improve global optimization performance. The strategy is a heuristic algorithm and it exploits not only multiple surrogates, but also multiple optimizers. Multiple optimizations of multiple surrogate models yield multiple candidate design points of optima. During the sequential sampling process, the algorithm ranks candidate design points, selects the points as many as specified, and builds the improved surrogate model. Various mathematical functions with different numbers of design variables are chosen to compare the proposed method with the other most recent algorithm, MSEGO. The proposed method shows superior performance to the other method.

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