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  • A parallel interval computation model with alternative message passing

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    65052_1.pdf (282.5Kb)
    Author(s)
    Wu, Y
    Kumar, A
    Shi, P
    Griffith University Author(s)
    Wu, Yong
    Year published
    2010
    Metadata
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    Abstract
    In this paper, we propose a decentralized parallel computation model for global optimization using interval analysis. The model is adaptive to any number of processors and there is no need to design an initial decomposition scheme to feed each processor at the beginning. The work load is distributed evenly among all processors by alternative message passing. Numerical experiments indicate that the model works well and is stable with different number of parallel processors, distributes the load evenly among the processors, and provides an impressive speedup, especially when the problem is timeconsuming to solve.In this paper, we propose a decentralized parallel computation model for global optimization using interval analysis. The model is adaptive to any number of processors and there is no need to design an initial decomposition scheme to feed each processor at the beginning. The work load is distributed evenly among all processors by alternative message passing. Numerical experiments indicate that the model works well and is stable with different number of parallel processors, distributes the load evenly among the processors, and provides an impressive speedup, especially when the problem is timeconsuming to solve.
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    Conference Title
    Proceedings - 2010 2nd International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2010
    Volume
    2
    DOI
    https://doi.org/10.1109/IHMSC.2010.129
    Copyright Statement
    © 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    Subject
    Optimisation
    Pervasive computing
    Publication URI
    http://hdl.handle.net/10072/40023
    Collection
    • Conference outputs

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