$\require{mediawiki-texvc}$
  • 검색어에 아래의 연산자를 사용하시면 더 정확한 검색결과를 얻을 수 있습니다.
  • 검색연산자
검색연산자 기능 검색시 예
() 우선순위가 가장 높은 연산자 예1) (나노 (기계 | machine))
공백 두 개의 검색어(식)을 모두 포함하고 있는 문서 검색 예1) (나노 기계)
예2) 나노 장영실
| 두 개의 검색어(식) 중 하나 이상 포함하고 있는 문서 검색 예1) (줄기세포 | 면역)
예2) 줄기세포 | 장영실
! NOT 이후에 있는 검색어가 포함된 문서는 제외 예1) (황금 !백금)
예2) !image
* 검색어의 *란에 0개 이상의 임의의 문자가 포함된 문서 검색 예) semi*
"" 따옴표 내의 구문과 완전히 일치하는 문서만 검색 예) "Transform and Quantization"
쳇봇 이모티콘
안녕하세요!
ScienceON 챗봇입니다.
궁금한 것은 저에게 물어봐주세요.

논문 상세정보

Abstract

The optimum grillage design belongs to nonlinear constrained optimization problem. The determination of beam scantlings for the grillage structure is a very crucial matter out of whole structural design process. The performance of optimization methods, based on penalty functions, is highly problem-dependent and many methods require additional tuning of some variables. This additional tuning is the influences of penalty coefficient, which depend strongly on the degree of constraint violation. Moreover, Binary-coded Genetic Algorithm (BGA) meets certain difficulties when dealing with continuous and/or discrete search spaces with large dimensions. With the above reasons, Real-coded Micro-Genetic Algorithm ($R{\mu}GA$) is proposed to find the optimum beam scantlings of the grillage structure without handling any of penalty functions. $R{\mu}GA$ can help in avoiding the premature convergence and search for global solution-spaces, because of its wide spread applicability, global perspective and inherent parallelism. Direct stiffness method is used as a numerical tool for the grillage analysis. In optimization study to find minimum weight, sensitivity study is carried out with varying beam configurations. From the simulation results, it has been concluded that the proposed $R{\mu}GA$ is an effective optimization tool for solving continuous and/or discrete nonlinear real-world optimization problems.

참고문헌 (12)

  1. Caruana, R. A. and Schaffer, J. D. (1988), 'Representation and hidden bias: gray versus binary coding for genetic algorithms,' Proc. of the Fifth Int. Conf. on Machine Learning, pp. 153-162 
  2. Davis, L. (1989), 'Adapting operator probabilities in genetic algorithms,' Proc. of the Third Int. Conf. on Genetic Algorithms, J. David Schaffer (Ed.), Morgan Kaufmann Publishers, San Mateo, pp. 61-69 
  3. Goldberg, D. E. (1989), 'Genetic algorithms and walsh functions: part II, deception and its analysis,' Complex Systems, Vol. 3, pp. 153-171 
  4. Herrera, F., Lozano, M., and Verdegay, J. L. (1998), 'Tackling real-coded genetic algorithms: operators and tools for behavioural analysis,' Artificial Intelligence Review, Vol. 12, No. 4, pp. 265-319 
  5. Kim, K. S., Kim, K. S., and Park, H. J. (2004), 'A Study on the Analysis and Design of Grillages under a Worst Point Load,' Key Engineering Materials, Vol. 261-263, pp. 783-788 
  6. Kim, Y., Cho, Park, M. C., Gotch, J. W., K., and Toyosada, M. (2003), 'Optimum Design of Sandwich Panel using Hybrid Metaheuristics Approach,' Int. J of Ocean Engineering and Technology, Vol. 17, No. 6, pp. 38-46 
  7. Kim Y., Kim, B. I., and Shin, S. C. (2005), 'Real-coded Micro-Genetic Algorithm for Nonlinear Constrained Engineering Designs', J. of Ship & Ocean Technology (SOTECH), Vol. 9, No. 4, pp. 35-46 
  8. Kim, Y., Kim, K. S., and Park, J. W. (2006), 'Midship Section Optimization of Hatchcoverless Container Ship based on Real-Coded Micro-Genetic Algorithm,' Key Engineering Materials, Vol. 306-308, pp. 529-534 
  9. Koziel, S., and Michalewicz, Z. (1999), 'Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization,' Evolutionary Computation, Vol. 7, No. 1, pp. 19-44 
  10. Lloyd's Register of shipping (2003), 'Rules and Regulations for the classification of ship' 
  11. Michalewicz, Z. (1994) 'Genetic Algorithms + Data Structures = Evolution Programs,' extended edition, Springer-Verlag, New York 
  12. Radcliffe, N. J. (1992), 'Non-Linear Genetic Representation,' Parallel Problem Solving from Nature 2, R. Manner and B. Manderick (Ed.), Elsevier Science Publishers, Amsterdam, pp. 259-268 

이 논문을 인용한 문헌 (0)

  1. 이 논문을 인용한 문헌 없음

원문보기

원문 PDF 다운로드

  • ScienceON :
  • KCI :

원문 URL 링크

원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다. (원문복사서비스 안내 바로 가기)

상세조회 0건 원문조회 0건

DOI 인용 스타일