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A Parallel Interval Computation Model for Global Optimization with Automatic Load Balancing

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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 the workload is automatically and evenly distributed among all processors by alternative message passing. The problems received by each processor are processed based on their local dominance properties, which avoids unnecessary interval evaluations. Further, the problem is treated as a whole at the beginning of computation so that no initial decomposition scheme is required. 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.

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Authors and Affiliations

  1. Department of International Business and Asian Studies, Griffith University, Melbourne, QLD 4222, Australia

    Yong Wu

  2. School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, Melbourne, Australia

    Arun Kumar

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  1. Yong Wu
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  2. Arun Kumar
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Correspondence to Yong Wu.

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Wu, Y., Kumar, A. A Parallel Interval Computation Model for Global Optimization with Automatic Load Balancing. J. Comput. Sci. Technol. 27, 744–753 (2012). https://doi.org/10.1007/s11390-012-1260-x

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  • DOI: https://doi.org/10.1007/s11390-012-1260-x

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