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Multi-Level Optimization for Steel Frames using Discrete Variables  

조효남 (한양대학교 토목, 환경공학과)
민대홍 (안산공과대학)
박준용 (대한 콘설탄트)
Publication Information
Journal of the Computational Structural Engineering Institute of Korea / v.15, no.3, 2002 , pp. 453-462 More about this Journal
Abstract
Discrete-sizing or standardized steel profiles are used in steel design and construction practice. However, most of numerical optimization methods follow additional step(round-up discrete-sizing routine) to use the standardized steel section profiles, and accordingly the optimality of the resulting design nay be doubtful. Thus, in this paper, an efficient multi-level optimization algorithm is proposed to improve the shortcoming of the conventional optimization methods using the round-up discrete-sizing routine. Also, multi-level optimization technique with a decomposition method that separates both system-level and element-level is incorporated in the algorithm to enhance the performance of the proposed algorithms. The proposed algorithm is expected to achieve considerable improvement on both the efficiency of the numerical process and the accuracy of the global optimum.
Keywords
multi- level optimization; discrete- variables; standard steel profile;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Schoonover, P. L., 'Application of a genetic algorithm to the optimization of hybrid rockets', Journal of Spacecraft and Rockets, 37, 2000, pp.622-629   DOI   ScienceOn
2 Ghasemi, M. R., 'Optimization of trusses using genetic algorithms for discrete and continuous variables', Engineering Computations, 16, 1999, pp.272-301   DOI   ScienceOn
3 Lee, DK., 'Hybrid system approach to optimum design of a ship,' Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol. 13, 1999, pp.1-11
4 Chattopadhyay, A., 'Multilevel decomposition procedure for efficient design optimization of helicopter rotor blades,' AIAA Journal, Vol. 33, 1995, pp.223-230   DOI   ScienceOn
5 Griewank, A., and Corliss, G. F., Automatic differentiation of algorithms : thoery, implementation, and application. Society of Industrial and Applied Mathematics, Philadelphia, Pa. 1991
6 Camp C., Pezeshk S., and Cao G.(1998) 'optimized design of using genetic algorithm', ASCE Journal of structural engineering, Vol. 124, No. 5, pp.551-559   DOI   ScienceOn
7 Ip, W. H., 'Multi-product planning and scheduling using genetic algorithm approach', Computers & Industrial Engineering, 38, 2000, pp.283-296   DOI   ScienceOn
8 Bichof, C., ADIFOR-generating derivative codes from FORTRAN 77 programs, Scientific Programming, 1992
9 Bischof, C., The ADIFOR2.0 system for the automatic differentiation of FORTRAN 77 programs, IEEE Computational Sci. & Eng., 3(3), 1996, pp.18-32   DOI   ScienceOn
10 Ghasemi, M. R., 'Optimization of trusses using genetic algorithms for discrete and continuous variables', Engineering Computations, 16, 1999, pp.272-301   DOI   ScienceOn
11 Berz, M., Computational differentiation-techniques, tools, and applications, Society for Industrial and Applied Mathmatics, Philadelphia, 1996
12 Sobieszczanski-Sobieski, J., 'Structural optimization by multilevel decomposition,' AIAA Journal, Vol. 23, 1985, pp.1775-1782   DOI   ScienceOn
13 El-Beltagy, M. A. and Keane, A. J., 'A comparison of various optimization algorithms on a multilevel problem', Artificial Intelligence, 12, 1999, pp.639-654
14 조효남, 민대홍, 박준용, 이산형변수를 이용한 뼈대구조물의 다단계 최적설계, 2000년 가을 전산구조공학회 학술발표회 논문집 제13권, 제20집
15 Kirsch, U., 'Multilevel approach to optimum structural design,' ASCE Journal of the Structural Division, Vol. 101, 1975, pp.957-974
16 Lust, R. V., Schmit, L. A., 'Alternative Approximation Concepts for Space Frame Synthesis', AIAA/ASME/ASCE/AHS 26th Structures, Structural Dynamics and Materials Conference, April 82, 1985, pp.333-348
17 Gang, L., Multiobjective and multilevel optimization for steel frames, Engineering Structures, 21, 1999, pp.519-529   DOI   ScienceOn
18 Li, QS., 'Multilevel optimal design of builings with active control under winds using genetic algorithms,' Journal of Wind Engineering and Industrial Aerodynamics, Vol. 86, No. 1, 2000, pp.65-86   DOI   ScienceOn
19 Goldberg, D. E., Computer-Aided Gas pipeline using Genetic Algorithm and Rule Learning, Ph.D. thesis, Dept. Civil Eng., Univ. Michigan., 1983.
20 AISC., Load and Resistance Factor Design Manual of steel construction, American Institute of Steel Construction, Chicago, 1994, p.126-143
21 Kumar, R., Rockett, P., 'Multiobjective genetic algorithm partitioning for hierarchical learning of high-dimensional pattern spaces: a learning- follows-de-composition strategy,' IEEE Transactions on Neural Networks, Vol. 9, No. 5, 1998, pp.822-830   DOI   ScienceOn
22 AISC., Seismic provisions for structural steel buildings, American Institute of Steel Construction, Chicago, 1992
23 AISC., Load and resistance factor design specification for structural steel buildings, American Institute of Steel Construction, Chicago, 1993
24 Vanderplaats, G. N., ASD : A FORTRAN Program for Automated Design Synthesis, Engineering Design Optimization, Inc, Santa Barbara, 1985
25 Okamoto, M., 'Nonlinear numerical optimization with use of a hybrid Genetic Algorithm incorporating the Modified Powell method,' Applied Mathematics and Computation, Vol. 91, 1998, pp.63-72   DOI   ScienceOn
26 Chattopadhyay, A., Pagaldipti, N., 'A multidisciplinary optimization using semianalytical sensitivity analysis procedure and multilevel decomposition,' Computers Math & Applic, Vol. 29, 1995, pp.55-66