A noise annealing neural network for global optimization

This paper deals with a neural network model for global optimization. The model presented can solve nonlinear constrained optimization problems with continuous decision variables. Incorporating the noise annealing concept, the model is able to produce such a solution which is the global optima of the original task with probability close to 1. After a brief outline of some existing globally optimizing neural networks we introduce the stochastic neural model called noise annealing neural network which is based on Wong´s diffusion machine and can be regarded as an extension of the canonical nonlinear programming neural network by Kennedy-Chua (1987). The usefulness of the model developed is supported by analytical investigations and computer simulations