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A Study on the Optimization Method using the Genetic Algorithm with Sensitivity Analysis

민감도가 고려된 알고리듬을 이용한 최적화 방법에 관한 연구

Lee, Jae-Gwan;Sin, Hyo-Cheol
이재관;신효철

  • Published : 2000.06.01

Abstract

A newly developed optimization method which uses the genetic algorithm combined with the sensitivity analysis is presented in this paper. The genetic algorithm is a probabilistic method, searching the optimum at several points simultaneously, requiring only the values of the object and constraint functions. It has therefore more chances to find global solution and can be applied various problems. Nevertheless, it has such shortcomings that even it approaches the optimum rapidly in the early stage, it slows down afterward and it can't consider the constraints explicitly. It is only because it can't search the local area near the current points. The traditional method, on the other hand, using sensitivity analysis is of great advantage in searching the near optimum. Thus the combination of the two techniques makes use of the individual advantages, that is, the superiority both in global searching by the genetic algorithm and in local searching by the sensitivity analysis. Application of the method to the several test functions verifies that the method suggested is very efficient and powerful to find the global solutions, and that the constraints can be considered properly.

Keywords

Optimal Design;Genetic Algorithm;Sensitivity Analysis;Global Solution;Probabilistic Search

References

  1. Voigt, H. M., Ebeling, W., Rechenberg, I. and Schwefel, H. P.(Eds.), 1996, Parallel Problem Solving from Nature-PPSN IV, Springer-Verlag
  2. Davidor, Y. Schwefel, H. P. and Manner R. (Eds.), 1991, Parallel Problem Solving from Nature-PPSN III, Springer-Verlag
  3. Darwen, P and Yao, X., 'Every Niching Method has its Niche: Fitness Sharing and Implicit Sharing Compared,' In Voigt and Ebeling[13]
  4. Gruninger T. and Wallace, D. 1996, 'Multimodal Optimization using Genetic Algorithms,' MIT CAD lab. Technical Report.
  5. Heistermann J., 'Different Learning Algorithms for Neural Networks-A Comparative Study,' In Schwefel and Manner[12]
  6. Chu K. C. and Gang F. 1995, 'Accelerated Genetic Algorithms: Combined with Local Search Techniques for Fast and Accurate Global Search ,' IEEE International Conference on Evolutionary Computation, Vol. 1 p. 378 https://doi.org/10.1109/ICEC.1995.489177
  7. Holland, J. H. 1975, 'Adaptation in Natural and Artificial Systems,' The University of Michigan Press, Ann Arbor, MI
  8. Goldberg, D. E. and Kuo, C. H. 1987, 'Genetic Algorithms in Pipeline Optimization,' J. of Computers in Civil Engineering, Vol. 1 No. 2 pp. 128-141
  9. Galante, M. 1996, 'Genetic Algorithms as an Approach to Optimize Real-world Trusses,' Int. J. for Numerical Method in Engineering, Vol. 39 pp. 361-382 https://doi.org/10.1002/(SICI)1097-0207(19960215)39:3<361::AID-NME854>3.0.CO;2-1
  10. Deb, K. 1991, 'Optimal Design of a welded Beams via Genetic Algorithms,' AIAA Journal, Vol. 29 No. 11 pp. 2013-2015
  11. Jenkins, W. M. 1991, 'Structural Optimization with the Genetic Algorithm,' The Structural Engineer Vol. 69 No. 24 pp. 418-422
  12. Arora, J. S. 1989, Applied Optimal Design; Mechanical and Structural Systems, University of Iowa
  13. Arora, J. S., Elwakeil O. A. and Chajande, A. I. 1995, 'Global Optimization Method for Engineering Applications: a Review,' Structural Optimization 9, pp. 137-159 https://doi.org/10.1007/BF01743964