Abstract
In this paper, we introduce a novel distributed Mean field Genetic algorithm called MGA for the resource allocation problems in MPI environments, which is an important issue in parallel processing. The proposed MGA is a hybrid algorithm of Mean Field Annealing (MFA) and Simulated annealing-like Genetic Algorithm (SGA). SGA uses the Metropolis criteria for state transition as in simulated annealing to keep the convergence property in MFA. The proposed MGA combines the benefit of rapid convergence property of MFA and the effective genetic operations of SGA. Our experimental results indicate that the composition of heuristic mapping methods improves the performance over the conventional ones in terms of communication cost, load imbalance and maximum execution time.
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© 2006 Springer-Verlag Berlin Heidelberg
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Hong, C. (2006). A Distributed Hybrid Heuristics of Mean Field Annealing and Genetic Algorithm for Load Balancing Problem. In: Greco, S., et al. Rough Sets and Current Trends in Computing. RSCTC 2006. Lecture Notes in Computer Science(), vol 4259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11908029_75
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DOI: https://doi.org/10.1007/11908029_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47693-1
Online ISBN: 978-3-540-49842-1
eBook Packages: Computer ScienceComputer Science (R0)