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Modified Efficient Global Optimization for a Hat-Stiffened Composite Panel with Buckling Constraint

机译:具有屈曲约束的帽子加筋复合板的改进高效全局优化

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The optimization method for composite structural components described herein uses modified efficient global optimization with a multi-objective genetic algorithm and a kriging response surface. For efficient global optimization using kriging, the kriging response surface is used as a representative of the function value. The stochastic distribution of the kriging is used to improve the estimation error of the kriging surrogate model. Using efficient global optimization, a hat-stiffened composite panel was optimized to reduce the weight with the buckling load constraint. The expected improvement was used as a single objective function of a particle swarm optimization. Nevertheless, it is difficult to obtain a feasible solution that satisfies buckling load constraints with the progress of optimization. Using a multi-objective genetic algorithm, we obtain the feasible optimal structure satisfying the constraints. The expected improvement objective function is divided into two objective functions: weight reduction and the uncertainty of satisfaction of the buckling load constraint. Kriging approximation, which is improved with the selected Pareto optimal frontier, reduces the computational cost. Also, a genetic algorithm is used to optimize the stiffened panel configuration. The fractal branch-and-bound method is used for stacking sequence optimizations. This method obtained a feasible optimal structure at a low computational cost.
机译:本文所述的用于复合结构部件的优化方法使用具有多目标遗传算法和克里金响应面的改进的有效全局优化。为了使用克里金法进行有效的全局优化,将克里金法响应面用作函数值的代表。使用克里格的随机分布来改善克里格代理模型的估计误差。通过有效的全局优化,对帽子加硬的复合板进行了优化,以减轻重量并限制屈曲载荷。预期的改进被用作粒子群优化的单个目标函数。然而,随着优化的进展,难以获得满足屈曲载荷约束的可行解决方案。使用多目标遗传算法,我们得到了满足约束条件的可行的最优结构。预期的改进目标函数分为两个目标函数:重量减轻和屈曲载荷约束满足的不确定性。通过选择的帕累托最优边界改进了克里格近似,从而降低了计算成本。而且,遗传算法被用来优化加劲板的构型。分形分支定界方法用于堆叠序列优化。该方法以较低的计算成本获得了可行的最佳结构。

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