论文标题
神经优化机器：一种用于优化的神经网络方法
Neural Optimization Machine: A Neural Network Approach for Optimization
论文作者
论文摘要
提出了一种新型的神经网络（NN）方法，以进行约束优化。所提出的方法使用特殊设计的NN体系结构和训练/优化程序，称为神经优化机（NOM）。 NN模型近似NOM的目标函数。优化过程由神经网络的内置反向传播算法进行。 NOM通过扩展NN目标函数模型的体系结构来解决优化问题。这是通过适当设计NOM的结构，激活功能和损耗功能来实现的。 NN目标函数可以具有任意体系结构和激活功能。 NOM的应用不仅限于特定的优化问题，例如线性和二次编程。结果表明，设计变量维度的增加不会大大增加计算成本。然后，将NOM扩展为多目标优化。最后，使用数值优化问题对NOM进行测试，并应用于加成制造中处理参数的最佳设计。
A novel neural network (NN) approach is proposed for constrained optimization. The proposed method uses a specially designed NN architecture and training/optimization procedure called Neural Optimization Machine (NOM). The objective functions for the NOM are approximated with NN models. The optimization process is conducted by the neural network's built-in backpropagation algorithm. The NOM solves optimization problems by extending the architecture of the NN objective function model. This is achieved by appropriately designing the NOM's structure, activation function, and loss function. The NN objective function can have arbitrary architectures and activation functions. The application of the NOM is not limited to specific optimization problems, e.g., linear and quadratic programming. It is shown that the increase of dimension of design variables does not increase the computational cost significantly. Then, the NOM is extended for multiobjective optimization. Finally, the NOM is tested using numerical optimization problems and applied for the optimal design of processing parameters in additive manufacturing.
