Variable stiffness (VS) composite structures can greatly increase the composite designability and thus have attracted much attention in recent years. This paper focuses on the maximization of load carrying capacity of VS composite cylinders under different loading cases, and the multi-objective optimization method is used to get the optimal results. First, the VS composite cylinder is optimized under four single loading cases. The results show that the anti-buckling capacity of the VS cylinder is better than the constant stiffness (CS) counterpart. The active learning Kriging surrogate model (AK) is applied to the cylinder optimization and the accuracy and efficiency of AK are verified. Under combined loading cases, the multi-objective particle swarm optimization is used to obtain the final Pareto-optimal solutions. The results indicate that the loading capacity of the VS cylinder is much greater than CS cylinder in the cases studied.

中文翻译：

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基于多目标优化方法的变刚度复合圆柱体承载能力最大化

变刚度（VS）复合材料结构可以极大地提高复合材料的可设计性，因此近年来备受关注。本文重点研究了VS复合气缸在不同载荷工况下的承载能力最大化问题，采用多目标优化方法得到最优结果。首先，VS复合气缸在四种单一载荷工况下进行了优化。结果表明，VS圆柱体的抗屈曲能力优于恒定刚度（CS）圆柱体。将主动学习克里金代理模型（AK）应用于圆柱优化，验证了AK的准确性和效率。在组合载荷情况下，采用多目标粒子群优化算法得到最终的帕累托最优解。