Neural networks for solving Constrained Steiner Tree problem

Chotipat Pornavalai, Goutam Chakraborty, Norio Shiratori

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Citations (Scopus)

    Abstract

    Hopfield neural network model for finding an optimal or 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 find an optimal tree (least total cost tree) from a source node to a number of destination nodes, where each path from source to a destination must satisfy a constraint condition (delay bound condition). This problem is called Constrained Steiner Tree (CST) problem, and was proved to he a NP-complete. A new adaptive coefficient control method for the proposed Hopfield energy function is also developed. Through computer simulation, it is shown that the proposed model could always find a near-optimal valid solution.

    Original languageEnglish
    Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
    Place of PublicationPiscataway, NJ, United States
    PublisherIEEE
    Pages1867-1870
    Number of pages4
    Volume4
    Publication statusPublished - 1995
    EventProceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6) - Perth, Aust
    Duration: 1995 Nov 271995 Dec 1

    Other

    OtherProceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6)
    CityPerth, Aust
    Period95/11/2795/12/1

    ASJC Scopus subject areas

    • Software

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  • Cite this

    Pornavalai, C., Chakraborty, G., & Shiratori, N. (1995). Neural networks for solving Constrained Steiner Tree problem. In IEEE International Conference on Neural Networks - Conference Proceedings (Vol. 4, pp. 1867-1870). IEEE.