基于神经网络和粒子群算法的环肋圆柱壳优化设计

对于潜艇外壳等外压容器来说，满足稳定性要求至关重要。本文利用Matlab编写改进粒子群算法优化程序，利用 Ansys的 Apdl语言完成了环肋圆柱壳的参数化建模，以圆柱壳厚度、肋骨尺寸和肋距作为离散设计变量，以稳定性要求作为约束条件，构造了合适的惩罚函数，以质量最轻作为设计目标，实现了基于 BP神经网络和粒子群算法的环肋圆柱壳优化设计。在优化过程中，首先采用拉丁超立方体抽样完成了样本点的选取，然后对样本点进行有限元分析，根据有限元分析结果构建 BP神经网络代理模型，并探讨了样本点数量对代理模型预测精度的影响，最后采用改进粒子群算法对代理模型进行优化。优化结果表明，对于需要考虑离散变量和复杂非线性约束的结构优化问题，采用 BP神经网络和粒子群算法联合优化的方法能够节省大量计算时间，并达到理想的优化效果。

Optimization of ring-stiffened cylindrical shell based on neural network and particle swarm optimization algorithm

For the external pressure vessel such as submarine shell, it is important to meet stability requirement. This paper wrote an improved particle swarm optimization(PSO), in which the penalty function is employed to transform nonlinear constraint optimization to unconstrained optimization. Then based on Matlab and Ansys, BP neural network and particle swarm optimization were applied to optimize ring-stiffened cylindrical shell , with the stability as constraint, with the total mass of stiffened cylindrical as objective function. And the optimal variables are shell thickness, frame dimensions and frame spacing. In the process of optimization, latin hypercube sampling method are used to choose sample points, and the finite element analysis was carried out on the sample points. With the analysis result, BP neural network can be built. Then this paper discuss the sample’s influence on prediction accuracy of neural network. Finally, this paper optimize the neural network with improved particle swarm optimization method. Optimum results shows that the validity of the proposed approach is examined, and this method can be used to solve nonlinear constraints discrete structural optimization problems. Use BP-PSO optimization algorithm can get good optimization result and save lots of time.