Browsing by Author "Gantovnik, Vladimir B."

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    A Genetic Algorithm for Mixed Integer Nonlinear Programming Problems Using Separate Constraint Approximations
    Gantovnik, Vladimir B.; Gürdal, Zafer; Watson, Layne T.; Anderson-Cook, Christine M. (Department of Computer Science, Virginia Polytechnic Institute & State University, 2003)
    This paper describes a new approach for reducing the number of the fitness and constraint function evaluations required by a genetic algorithm (GA) for optimization problems with mixed continuous and discrete design variables. The proposed additions to the GA make the search more effective and rapidly improve the fitness value from generation to generation.The additions involve memory as a function of both discrete and continuous design variables, and multivariate approximation of the individual functions' responses in terms of several continuous design variables. The approximation is demonstrated for the minimum weight design of a composite cylindrical shell with grid stiffeners.
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    A genetic algorithm with memory for mixed discrete-continuous design optimization
    Gantovnik, Vladimir B.; Anderson-Cook, Christine M.; Gürdal, Zafer; Watson, Layne T. (Department of Computer Science, Virginia Polytechnic Institute & State University, 2003)
    This paper describes a new approach for reducing the number of the fitness function evaluations required by a genetic algorithm (GA) for optimization problems with mixed continuous and discrete design variables. The proposed additions to the GA make the search more effective and rapidly improve the fitness value from generation to generation. The additions involve memory as a function of both discrete and continuous design variables, multivariate approximation of the fitness function in terms of several continuous design variables, and localized search based on the multivariate approximation. The approximation is demonstrated for the minimum weight design of a composite cylindrical shell with grid stiffeners.
  • Genetic Algorithm with Memory for Optimal Design of Laminated Sandwich Composite Site Panels
    Gantovnik, Vladimir B.; Gürdal, Zafer; Watson, Layne T. (Department of Computer Science, Virginia Polytechnic Institute & State University, 2002)
    This paper is concerned with augmenting genetic algorithms (GAs) to include memory for continuous variables, and applying this to stacking sequence design of laminated sandwich composite panels that involves both discrete variables and a continuous design variable. The term “memory” implies preserving data from previously analyzed designs. A balanced binary tree with nodes corresponding to discrete designs renders efficient access to the memory. For those discrete designs that occur frequently, an evolving database of continuous variable values is used to construct a spline approximation to the fitness as a function of the single continuous variable. The approximation is then used to decide when to retrieve the fitness function value from the spline and when to do an exact analysis to add a new data point for the spline. With the spline approximation in place, it is also possible to use the best solution of the approximation as a local improvement during the optimization process. The demonstration problem chosen is the stacking sequence optimization of a sandwich plate with composite face sheets for weight minimization subject to strength and buckling constraints. Comparisons are made between the cases with and without the binary tree and spline interpolation added to a standard genetic algorithm. Reduced computational cost and increased performance index of a genetic algorithm with these changes are demonstrated.