论文标题
使用深卷积网络的替代辅助辅助生成设计
Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks
论文作者
论文摘要
在本文中，提出了一种多目标进化替代辅助方法，用于沿海防波堤的快速生成设计。为了近似计算昂贵的目标函数，深层卷积神经网络被用作替代模型。该模型允许优化具有不同数量的结构和段的防波堤的配置。除替代物外，还开发了助理模型来估计预测的信心。在合成水域测试了所提出的方法，使用天鹅模型来计算波高。实验结果证实，所提出的方法允许在同一时间获得比非溶液方法更有效（具有更好的保护性能）溶液。
In the paper, a multi-objective evolutionary surrogate-assisted approach for the fast and effective generative design of coastal breakwaters is proposed. To approximate the computationally expensive objective functions, the deep convolutional neural network is used as a surrogate model. This model allows optimizing a configuration of breakwaters with a different number of structures and segments. In addition to the surrogate, an assistant model was developed to estimate the confidence of predictions. The proposed approach was tested on the synthetic water area, the SWAN model was used to calculate the wave heights. The experimental results confirm that the proposed approach allows obtaining more effective (less expensive with better protective properties) solutions than non-surrogate approaches for the same time.
