A parallel interval computation model with alternative message passing
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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.
2010 2nd International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)
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