Neural Optimization Machine: A Neural Network Approach for Optimization
- URL: http://arxiv.org/abs/2208.03897v1
- Date: Mon, 8 Aug 2022 03:34:58 GMT
- Title: Neural Optimization Machine: A Neural Network Approach for Optimization
- Authors: Jie Chen, Yongming Liu
- Abstract summary: 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)
- Score: 10.283797653337132
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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