DOI QR코드

DOI QR Code

A Non-Uniform Convergence Tolerance Scheme for Enhancing the Branch-and-Bound Method

비균일 수렴허용오차 방법을 이용한 분지한계법 개선에 관한 연구

  • 정상진 (한양대학교 대학원 기계공학과) ;
  • ;
  • 최경현 (한양대학교 산업공학과) ;
  • 최동훈 (한양대학교 최적설계신기술연구센터)
  • Received : 2011.01.27
  • Accepted : 2012.01.17
  • Published : 2012.04.01

Abstract

In order to improve the efficiency of the branch-and-bound method for mixed-discrete nonlinear programming, a nonuniform convergence tolerance scheme is proposed for the continuous subproblem optimizations. The suggested scheme assigns the convergence tolerances for each continuous subproblem optimization according to the maximum constraint violation obtained from the first iteration of each subproblem optimization in order to reduce the total number of function evaluations needed to reach the discrete optimal solution. The proposed tolerance scheme is integrated with five branching order options. The comparative performance test results using the ten combinations of the five branching orders and two convergence tolerance schemes show that the suggested non-uniform convergence tolerance scheme is obviously superior to the uniform one. The results also show that the branching order option using the minimum clearance difference method performed best among the five branching order options. Therefore, we recommend using the "minimum clearance difference method" for branching and the "non-uniform convergence tolerance scheme" for solving discrete optimization problems.

혼합이산비선형계획법(mixed-discrete nonlinear programming) 문제의 최적화를 위한 대표적인 기법 중에 하나인 분지한계법(branch-and-bound method)은 다른 기법에 비해 강건하지만 분지한계법 내부의 각 노드마다 연속최적화를 수행해야 하기 때문에 많은 함수 계산이 요구되는 것으로 알려져 있다. 이러한 분지한계법의 단점을 극복하기 위하여 크게 두 가지 연구를 수행하였다. 먼저, 분지한계법의 각 노드마다 동일한 수렴허용오차를 설정해주던 기존의 방법을 대체할 수 있는 비균일 수렴허용오차 방법을 제안하였다. 또한 분지한계법에 적용할 수 있는 5 가지 분지순서 방법 중에서 분지한계법의 성능을 가장 극대화할 수 있는 분지순서 방법을 제시하였다. 수렴허용오차 방법과 분지순서 방법들을 각각 선택하여 분지한계법에 적용한 후 7 개의 수학예제와 4 개의 공학예제에 대하여 테스트를 수행한 결과, 제안된 비균일 수렴허용오차 방법과 5 가지 분지순서 방법 중 최소간 격차이법을 분지한계법에 함께 적용할 경우 분지한계법의 성능이 가장 극대화 됨을 확인할 수 있었다.

Keywords

References

  1. Rajeev, S. and Krishnamoorthy, C. S., 1992, "Discrete Optimization of Structures Using Genetic Algorithms," Journal of Structural Engineering, Vol. 118, No. 5, pp. 1233-1250. https://doi.org/10.1061/(ASCE)0733-9445(1992)118:5(1233)
  2. Lin, C.-Y. and Hajela, P., 1992, "Genetic Algorithms in Optimization Problems with Discrete and Integer Design Variables," Engineering Optimization, Vol. 19, No. 4, pp. 309-327. https://doi.org/10.1080/03052159208941234
  3. He, S., Prempain, E. and Wu, Q. H., 2004, "An Improved Particle Swarm Optimizer for Mechanical Design Optimization Problems," Engineering Optimization, Vo. 36, No. 5, pp. 585-605. https://doi.org/10.1080/03052150410001704854
  4. Kitayama, S., Arakawa, M. and Yamazaki, K., 2006, "Penalty Function Approach for the Mixed Discrete Nonlinear Problems by Particle Swarm Optimization," Structural and Multidisciplinary Optimization, Vol. 32, No. 3, pp. 191-202. https://doi.org/10.1007/s00158-006-0021-2
  5. Shin, D. K., Gürdal, Z. and Griffin, O. H., 1990, "A Penalty Approach for Nonlinear Optimization with Discrete Design Variables," Engineering Optimization, Vol. 16, No. 1, pp. 29- 42. https://doi.org/10.1080/03052159008941163
  6. Loh, H. T. and Papalambros, P. Y., 1991, "A Sequential Linearization Approach for Solving Mixed- Discrete Nonlinear Design Optimization Problems," Journal of Mechanical Design, ASME, Vol. 113, No. 3, pp. 325-334. https://doi.org/10.1115/1.2912786
  7. Loh, H. T. and Papalambros, P. Y., 1991, "Computational Implementation and Tests of a Sequential Linearization Algorithm for Mixed-Discrete Nonlinear Design Optimization," Journal of Mechanical Design, ASME, Vol. 113, No. 3, pp. 335-345. https://doi.org/10.1115/1.2912787
  8. Bremicker, M., Papalambros, P. Y. and Loh, H. T., 1990, "Solution of Mixed-Discrete Structural Optimization Problems with a New Sequential Linearization Algorithm," Computers and Structures, Vo. 37, No. 4, pp. 451-461. https://doi.org/10.1016/0045-7949(90)90035-Z
  9. Land, A. H. and Doig, A. G., 1960, "An Automatic Method of Solving Discrete Programming Problems," Econometrica, Vol. 28, No. 3, pp. 497-520. https://doi.org/10.2307/1910129
  10. Dakin, R. J., 1965, "A Tree-Search Algorithm for Mixed Integer Programming Problems," The Computer Journal, Vol. 8, No. 3, pp. 250-255. https://doi.org/10.1093/comjnl/8.3.250
  11. Gupta, O. K. and Ravindran, A., 1983, "Nonlinear Integer Programming and Discrete Optimization," Journal of Mechanisms, Transmissions and Automation in Design, ASME, Vol. 105, No. 2, pp. 160-164. https://doi.org/10.1115/1.3258502
  12. Hajela, P. and Shih, C. J., 1989, "Optimal design of Laminated Composites Using a Modified Mixed Integer and Discrete Programming Algorithm," Computers and Structures, Vol. 32, No. 1, pp. 213-221. https://doi.org/10.1016/0045-7949(89)90087-4
  13. Sandgren, E., 1990, "Nonlinear Integer and Discrete Programming in Mechnical Design Optimization," Journal of Mechanical Design, ASME, Vol. 112, No. 2, pp. 223-229. https://doi.org/10.1115/1.2912596
  14. Arora, J. S., Huang, M. W. and Hsieh, C. C., 1994, "Methods for Optimization of Nonlinear Problems with Discrete Variables: A Review," Structural Optimization, Vol. 8, No. 2-3, pp. 69-85. https://doi.org/10.1007/BF01743302
  15. Tseng, C. H., Wang, L. W. and Ling, S. F., 1995, "Enhancing Branch-and-Bound Method for Structural Optimization," Journal of structural engineering, N.Y., Vol. 121, No. 5, pp. 831-837. https://doi.org/10.1061/(ASCE)0733-9445(1995)121:5(831)
  16. Leyffer, S., 2001, "Integrating SQP and Branch-and-Bound for Mixed Integer Nonlinear Programming," Computational Optimization and Applications, Vol. 18, No. 3, pp. 295-309. https://doi.org/10.1023/A:1011241421041
  17. Thanedar, P. B. and Vanderplaats, G. N., 1995, "Survey of Discrete Variable Optimization for Structural Design," Journal of Structural Engineering, Vol. 121, No. 2, pp. 301-305. https://doi.org/10.1061/(ASCE)0733-9445(1995)121:2(301)
  18. Huang, M. W. and Arora, J. S., 1997, "Optimal Design with Discrete Variables: Some Numerical Experiments," International Journal for Numerical Methods in Engineering, Vol. 40, No. 1, pp. 165-188. https://doi.org/10.1002/(SICI)1097-0207(19970115)40:1<165::AID-NME60>3.0.CO;2-I
  19. Jung, S., Choi, D. H. and Choi, G., "A sequential Quadratic Programming with an Approximate Hessian Matrix Update Using an Enhanced Two-Point Diagonal Quadratic Approximation," 13th AIAA/ISSMO Multi-Disciplinary Analysis and Optimization Conference, Fort Worth, Texas, USA.
  20. 1999, DOT user's manual, version 5.0, Vanderplaats Research & Development, Inc.
  21. 2011, PIAnO user's manual, version 3.3, PIDOTECH, Inc.
  22. Arora, J. S., 2004, Introduction to Optimum Design, 2nd Edition, Elsevier/Academic Press, pp. 517-518.
  23. Chvatal, V., 1983, Linear Programming, Freeman, W. H., New York.
  24. Salajegheh, E. and Vanderplaats, G. N., 1993, "Optimum Design of Trusses with Discrete Sizing and Shape Variables," Structural Optimization, Vol. 6, No. 2, pp. 79-85. https://doi.org/10.1007/BF01743339
  25. Arora, J. S., 2004, Introduction to optimum design, 2nd Edition, Elsevier/Academic Press, pp. 529.
  26. Chen, X., 2009, An Enhanced Branch-and-Bound Method for Discrete Optimization, Master's Thesis, Hanyang University, pp. 36-38.
  27. Arora, J. S., 2004, Introduction to Optimum Design, 2nd Edition, Elsevier/Academic Press, pp. 528.
  28. Rao, S. S. and Xiong, Y., 2005, "A hybrid Genetic Algorithm for Mixed-Discrete Design Optimization," Journal of Mechanical Design, Transactions of the ASME, Vol. 127, No. 6, pp. 1100-1112. https://doi.org/10.1115/1.1876436
  29. Nema, S., Goulermas, J., Sparrow, G. and Cook, P., 2008, "A Hybrid Particle Swarm Branch-and-Bound (HPB) Optimizer for Mixed Discrete Nonlinear Programming," IEEE Transactions on Systems, Man and Cybernetics Part A:Systems and Humans, Vol. 38, No. 6, pp. 1411-1424. https://doi.org/10.1109/TSMCA.2008.2003536