Abstract
The original Hopfield neural network model has been successfully applied for finding acceptable solutions to combinatorial optimization problems such as traveling salesperson problems and Hitchcock problems. However, in the original model, the formulated minimization problem of an energy function can never escape from the local minimum. By adding a Gaussian noise in the original neural networks, and using so-called annealing and sharpening schemes, it becomes possible to reach the global minimum of the energy function. In this paper, we focus on crisp and fuzzy 0-1 programming problems and examine the feasibility and efficiency of both the original and modified Hopfield neural network approaches via their energy minimization processes.
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