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A Parallel Interval Computation Model with Alternative Message Passing

Wu, Yong, Kumar, A and Shi, Peng (2010) A Parallel Interval Computation Model with Alternative Message Passing. In: 2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) 2010 : 26 - 28 Aug. 2010, Nanjing, Jiangsu, China, proceedings. IEEE Computer Society, Los Alamitos, California, pp. 120-123.

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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 time-consuming to solve.

Item Type: Book Section
ISBN: 9781424478699, 9780769541518
Uncontrolled Keywords: ResPubID19961, message passing, optimisation, parallel processing, decentralized parallel interval computation model, global optimization problem, interval analysis, message passing, parallel processing, parallel processors, branch-and-bound, computation model, global optimization, interval analysis, parallel processing, computational modeling, load management, load modeling, message passing, numerical models, program processors
Subjects: Historical > Faculty/School/Research Centre/Department > Institute for Logistics and Supply Chain Management (ILSCM)
Current > FOR Classification > 0802 Computation Theory and Mathematics
Historical > SEO Classification > 970108 Expanding Knowledge in the Information and Computing Sciences
Depositing User: VUIR
Date Deposited: 19 Jun 2013 00:56
Last Modified: 13 Mar 2015 00:35
URI: http://vuir.vu.edu.au/id/eprint/10053
DOI: https://doi.org/10.1109/IHMSC.2010.129
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Citations in Scopus: 0 - View on Scopus

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