A parallel interval computation model with alternative message passing

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Author(s)
Wu, Y
Kumar, A
Shi, P
Griffith University Author(s)
Year published
2010
Metadata
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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
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