Development of Optimization Algorithm for Unconstrained Problems Using the Sequential Design of Experiments and Artificial Neural Network

순차적 실험계획법과 인공신경망을 이용한 제한조건이 없는 문제의 최적화 알고리즘 개발

  • 이정환 (성균관대학교 대학원 기계공학부) ;
  • 서명원 (성균관대학교 기계공학부)
  • Published : 2008.03.01


The conventional approximate optimization method, which uses the statistical design of experiments(DOE) and response surface method(RSM), can derive an approximated optimum results through the iterative process by a trial and error. The quality of results depends seriously on the factors and levels assigned by a designer. The purpose of this study is to propose a new technique, which is called a sequential design of experiments(SDOE), to reduce a trial and error procedure and to find an appropriate condition for using artificial neural network(ANN) systematically. An appropriate condition is determined from the iterative process based on the analysis of means. With this new technique and ANN, it is possible to find an optimum design accurately and efficiently. The suggested algorithm has been applied to various mathematical examples and a structural problem.


Sequential Design of Experiments;Artificial Neural Network;Response Surface Method;Design of Experiments


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