Abstract
To find the global minimum of an NP-complete problem within a reasonable computational time is extremely difficult. The traveling salesman problem, in addition to being NP-complete, has a complicated solution set in terms of optimizing an energy function. A novel neural network that removes ambiguities in the solution set and eliminates local minima is described. This network obtains the global minimum at a small increase in computational time when compared to the Hopfield network. Salient features of this improved network are presented. >
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