Optimization by varied beam search in Hopfield networks

This paper shows that the performance of a Hopfield network for solving optimization problems can be improved by a varied beam search algorithm. The algorithm varies the beam search size and beam intensity during the network relaxation process. It consists of two stages: increasing the beam search parameters in the first stage, and then decreasing them in the second stage. The purpose of using such a scheme is to provide the network with a better chance to find more and better solutions. A large number of simulation results based on 200 randomly generated city distributions of the 10-city traveling salesman problem demonstrated that it is capable of increasing the percentage of valid tours by 28.3% and reducing the error rate by 40.8%, compared to the original Hopfield network