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A Novel Metamodel-based Simulation Optimization Algorithm using a Hybrid Sequential Experimental Design

Ajdari, Ali | 2012

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 42907 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Mahlooji, Hashem
  7. Abstract:
  8. In this work, we propose a metamodel-based simulation optimization algorithm using a novel hybrid sequential experimental design. The algorithm starts with a metamodel construction phase in which at each stage, a sequential experimental design is used to select a new sample point from the search space using a hybrid exploration-exploitation search strategy. Based on the available design points at each stage, a metamodel is constructed using Artificial Neural Network (ANN) and Kriging interpolation techniques. The resulting metamodel is then used in the optimization process to evaluate new solutions. We use Imperialist Competitive Algorithm (ICA) which is a powerful population-based evolutionary algorithm in the optimization phase to find near-optimal solution for the problem. The performance of the proposed algorithm is evaluated through comparing the results with a strong commercial experimental design toolbox called SUMO and Genetic Algorithm. The experiments show that the proposed algorithm outperforms its rival in both metamodel construction and the optimization phase. Moreover, we compare the performance of ANN and Kriging in terms of both speed and efficiency. The results indicate that while Kriging method outperforms ANN in term of speed, ANN is more competent for building metamodels for more complex surfaces
  9. Keywords:
  10. Simulation Optimization ; Metamodel ; Experiments Design ; Neural Network ; Genetic Algorithm ; Imperialist Competitive Algorithm ; Kriging Metamodel

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