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Neural network for optimal steiner tree computation

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Abstract

Hopfield neural network model for finding the shortest path between two nodes in a graph was proposed recently in some literatures. In this paper, we present a modified version of Hopfield model to a more general problem of searching an optimal tree (least total cost tree) from a source node to a number of destination nodes in a graph. This problem is called Steiner tree in graph theory, where it is proved to be a NP-complete. Through computer simulations, it is shown that the proposed model could always find an optimal or near-optimal valid solution in various graphs.

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Authors and Affiliations

  1. Research Institute of Electrical Communication, Tohoku University, 2-1-1 Katahira, Aoba-ku, 980, Sendai, Japan

    Chotipat Pornavalai & Norio Shiratori

  2. Software Center, The University of Aizu, Tsuruga, Ikki-Machi, 965-80, Aizu-Wakamatsu city, Japan

    Goutam Chakraborty

Authors
  1. Chotipat Pornavalai
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  2. Norio Shiratori
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  3. Goutam Chakraborty
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Pornavalai, C., Shiratori, N. & Chakraborty, G. Neural network for optimal steiner tree computation. Neural Process Lett 3, 139–149 (1996). https://doi.org/10.1007/BF00420283

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  • DOI: https://doi.org/10.1007/BF00420283

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