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A novel neural network (NN) approach is proposed for constrainedoptimization. The proposed method uses a specially designed NN architecture andtraining/optimization procedure called Neural Optimization Machine (NOM). Theobjective functions for the NOM are approximated with NN models. Theoptimization process is conducted by the neural network's built-inbackpropagation algorithm. The NOM solves optimization problems by extendingthe architecture of the NN objective function model. This is achieved byappropriately designing the NOM's structure, activation function, and lossfunction. The NN objective function can have arbitrary architectures andactivation functions. The application of the NOM is not limited to specificoptimization problems, e.g., linear and quadratic programming. It is shown thatthe increase of dimension of design variables does not increase thecomputational cost significantly. Then, the NOM is extended for multiobjectiveoptimization. Finally, the NOM is tested using numerical optimization problemsand applied for the optimal design of processing parameters in additivemanufacturing.

Neural Optimization Machine: A Neural Network Approach for Optimization