A Parallel Interval Computation Model for Global Optimization with Automatic Load Balancing
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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 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.
Journal of Computer Science and Technology
© 2012 Springer Boston. This is an electronic version of an article published in Journal of Computer Science and Technology, July 2012, Volume 27, Issue 4, pp 744-753. Journal of Computer Science and Technology is available online at: http://link.springer.com// with the open URL of your article.
Distributed Computing not elsewhere classified