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병렬 마이크로 유전자 알고리즘을 이용한 복합재 적층 구조물의 최적설계

Optimal Design of Laminated Stiffened composite Structures using a parallel micro Genetic Algorithm

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초록

In this paper, a parallel micro genetic algorithm was utilized in the optimal design of composite structures instead of a conventional genetic algorithm(SGA). Micro genetic algorithm searches the optimal design variables with only 5 individuals. The diversities from the nominal convergence and the re-initialization processes make micro genetic algorithm to find out the optimums with such a small population size. Two different composite structure optimization problems were proposed to confirm the efficiency of micro genetic algorithm compared with SGA. The results showed that micro genetic algorithm can get the solutions of the same level of SGA while reducing the calculation costs up to 70% of SGA. The composite laminated structure optimization under the load uncertainty was conducted using micro genetic algorithm. The result revealed that the design variables regarding the load uncertainty are less sensitive to load variation than that of fixed applied load. From the above-mentioned results, we confirmed micro genetic algorithm as a optimization method of composite structures is efficient.

Abstract

In this paper, a parallel micro genetic algorithm was utilized in the optimal design of composite structures instead of a conventional genetic algorithm(SGA). Micro genetic algorithm searches the optimal design variables with only 5 individuals. The diversities from the nominal convergence and the re-initialization processes make micro genetic algorithm to find out the optimums with such a small population size. Two different composite structure optimization problems were proposed to confirm the efficiency of micro genetic algorithm compared with SGA. The results showed that micro genetic algorithm can get the solutions of the same level of SGA while reducing the calculation costs up to 70% of SGA. The composite laminated structure optimization under the load uncertainty was conducted using micro genetic algorithm. The result revealed that the design variables regarding the load uncertainty are less sensitive to load variation than that of fixed applied load. From the above-mentioned results, we confirmed micro genetic algorithm as a optimization method of composite structures is efficient.

참고문헌 (13)

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  4. C. A. C. Coello, "A Micro-Genetic Algorithms for Multi-objective Optimization", Lecture Notes in Computer Science, 93, pp.126~143 (2000)
  5. 최성만, "마이크로 유전자 알고리즘을 이용한 구조 최적 설계", 석사, pp.2002
  6. A. R. de Faria, "On buckling optimization under uncertain loading combination", Structural and Multidisciplinary Optimization; journal of the International Society for Structural and Multidisciplinary Optimization, 21, pp.272~282 (2001)
  7. A. R. de Faria, "Buckling optimization and anti optimization of composite plates: uncertain loading combination", International Journal for Numerical Methods in Engineering, 53, pp.719~732 (2002)
  8. S. Adali, "Minimum weight design of symmetric angle-ply laminates under multiple uncertain load", Structural optimization, 9, pp.89~95 (1995)
  9. G. Venter, "A two species genetic algorithm for designing composite laminates subjected to uncertainty", Proceedings of the 37th AIAA, ASCE, pp.AHS, ASC[DOI]
  10. K. Huseyin, "Nonlinear theory of elastic stability" (1975), Noordhoff
  11. G. Syswerda, "Uniform crossover in genetic algorithms", Proceedings of the third International Conference on Genetic Algorithms, pp.2~9 (1989)
  12. C. Bisagni, "Post-buckling optimization of composite stiffened panels using neural networks", Composite Structures, 58, pp.237~247 (2002)
  13. "ABAQUS Analysis Users's Manual, Ver. 6.4"

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