A hill-jump algorithm of Hopfield neural network for shortest path problem in communication network |
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Authors: | Rong-Long Wang Shan-Shan Guo Kozo Okazaki |
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Affiliation: | (1) Faculty of Engineering, University of Fukui, Bunkyo 3-9-1, Fukui-shi 910-8507, Japan |
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Abstract: | In this paper, we present a hill-jump algorithm of the Hopfield neural network for the shortest path problem in communication
networks, where the goal is to find the shortest path from a starting node to an ending node. The method is intended to provide
a near-optimum parallel algorithm for solving the shortest path problem. To do this, first the method uses the Hopfield neural
network to get a path. Because the neural network always falls into a local minimum, the found path is usually not a shortest
path. To search the shortest path, the method then helps the neural network jump from local minima of energy function by using
another neural network built from a part of energy function of the problem. The method is tested through simulating some randomly
generated communication networks, with the simulation results showing that the solution found by the proposed method is superior
to that of the best existing neural network based algorithm. |
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Keywords: | Shortest path problem Combinatorial optimization problem Hopfield neural network Hill-jump algorithm Local minimum |
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