Browsing by Author "Nagendra, S."

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  • Design of Composite Laminates by a Genetic Algorithm with Memory
    Kogiso, N.; Watson, Layne T.; Gürdal, Zafer; Haftka, Raphael T.; Nagendra, S. (Department of Computer Science, Virginia Polytechnic Institute & State University, 1994)
    This paper describes the use of a genetic algorithm with memory for the design of minimum thickness composite laminates subject to strength, buckling and ply contiguity constraints. A binary tree is used to efficiently store and retrieve information about past designs. This information is used to construct a set of linear approximations to the buckling load in the neighborhood of each member of the population of designs. The approximations are then used to seek nearby improved designs in a procedure called local improvement. The paper demonstrates that this procedure substantially reduces the number of analyses required for the genetic search. The paper also demonstrates that the use of genetic algorithms helps find several alternate designs with similar performance, thus giving the designer a choice of alternatives.
  • Improved Genetic Algorithm for the Design of Stiffened Composite Panels
    Nagendra, S.; Jestin, D.; Gürdal, Zafer; Haftka, Raphael T.; Watson, Layne T. (Department of Computer Science, Virginia Polytechnic Institute & State University, 1994-10-01)
    The design of composite structures against buckling presents two major challenges to the designer. First, the problem of laminate stacking sequence design is discrete in nature, involving a small set of fiber orientations, which complicates the solution process. Therefore, the design of the stacking sequence is a combinatorial optimization problem which is suitable for genetic algorithms. Second, many local optima with comparable performance may be found. Most optimization algorithms find only a single optimum, while often a designer would want to obtain all the local optima with performance close to the global optimum. Genetic algorithms can easily find many near optimal solutions. However, they usually require very large computational costs. Previous work by the authors on the use of genetic algorithms for designing stiffened composite panels revealed both the above strength and weakness of the genetic algorithm. The present paper suggests several changes to the basic genetic algorithm developed previously, and demonstrates reduced computational cost and increased reliability of the algorithm due to these changes. Additionally, for a stiffened composite panel used in this study, designs lighter by about 4 percent compared to previous results were obtained.