DOI QR코드

DOI QR Code

구조물 최적설계를 위한 메타휴리스틱 알고리즘의 비교 연구

An Comparative Study of Metaheuristic Algorithms for the Optimum Design of Structures

  • 투고 : 2017.02.06
  • 심사 : 2017.03.03
  • 발행 : 2017.04.30

초록

Metaheuristic algorithms are efficient techniques for a class of mathematical optimization problems without having to deeply adapt to the inherent nature of each problem. They are very useful for structural design optimization in which the cost of gradient computation can be very expensive. Among them, the characteristics of simulated annealing and genetic algorithms are briefly discussed. In Metropolis genetic algorithm, favorable features of Metropolis criterion in simulated annealing are incorporated in the reproduction operations of simple genetic algorithm. Numerical examples of structural design optimization are presented. The example structures are truss, breakwater and steel box girder bridge. From the theoretical evaluation and numerical experience, performance and applicability of metaheuristic algorithms for structural design optimization are discussed.

키워드

참고문헌

  1. Boussaid, I. Lepagnot, J. & Siarry, P.(2013). A survey on optimization metaheuristics, Journal of Information Science, 237, 82-117. https://doi.org/10.1016/j.ins.2013.02.041
  2. Carroll, D. L.(1998). FORTRAN Genetic algorithms driver, Dept. of Aeronautical and Astronautical Engineering, University of Illinois at Urbana-Champaign.
  3. Goldberg, D. E.(1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley MA.
  4. Holland, J. H.(1992). Adaptation in Natural and Artificial Systems. A Bradford book, The MIT press.
  5. Jin, G. G.(2000). Genetic Algorithms and Their Applications (in Korean), Kyowoo-sa.
  6. Kirkpatrick, S. . Gelatt, C. D. & Vecchi, M. P.(1983). Optimization by simulated annealing, Science, 220 (4598): 671-680. https://doi.org/10.1126/science.220.4598.671
  7. Krishnakumar, K.(1989). Micro-genetic algorithms for stationary and non-stationary function optimization, Intelligent Control and Adaptive Systems, SPIE, 1196, 289-296.
  8. Metropolis, N. . Rosenbluth, A. W. . Rosenbluth, M. N. . Teller, A. H. & Teller, E.(1953). Equation of state calculations by fast computing machines, Journal of Chemical Physics, 21, 1087-1092. https://doi.org/10.1063/1.1699114
  9. Ryu, Y. S. . Lim, O. K. . Kim, M. K. & Lee, T. H. (2011). Introduction to Optimum Design (in Korean), Honreung Publishing co.
  10. Ryu, Y. S. . Kim, J. H. . Cho, H. M. & Kim, J. T. (2002). LRFD-based design optimization of steel box girder sections using genetic algorithms, Journal of Civil Engineering, KSCE, 6(2), 127-134. https://doi.org/10.1007/BF02829129
  11. Ryu, Y. S. . Park, K. B. . Kim, J. T. & Na, W. B. (2005). Optimum design of Composite breakwater with Metropolis GA, 6th World Congressof Structural Multidisciplinary Optimization, Rio de Janeiro, Brazil.
  12. Ryu, Y. S. . Park, K. B. . Kim, J. T. & Na, W. B. (2006). Development and efficiency evaluation of Metropolis GA for the structural optimization, Journal of the Computational Structural Engineering Institute of Korea, 19(1), 27-37.
  13. Van Laarhoven, P. J. M. & Aarts, E. H. L.(1987). Simulated Annealing - Theory and Applications. Kluwer Academic Publishers.
  14. Woo, B. H. & Park, H. S.(2003). Distributed Hybrid Genetic Algorithms for Structural Optimization, Journal of Computational Structural Engineering Institute of Korea, 16(5), 407-417.