Cooperated Bayesian algorithm for distributed scheduling problem |
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Authors: | Qiang Lei and Xiao Tian-yuan |
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Affiliation: | (1) Department of Automation, Tsinghua University, Beijing, 100084, China |
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Abstract: | This paper presents a new distributed Bayesian optimization algorithm (BOA) to overcome the efficiency problem when solving
NP scheduling problems. The proposed approach integrates BOA into the co-evolutionary schema, which builds up a concurrent
computing environment. A new search strategy is also introduced for local optimization process. It integrates the reinforcement
learning (RL) mechanism into the BOA search processes, and then uses the mixed probability information from BOA (post-probability)
and RL (pre-probability) to enhance the cooperation between different local controllers, which improves the optimization ability
of the algorithm. The experiment shows that the new algorithm does better in both optimization (2.2 %) and convergence (11.7
%), compared with classic BOA.
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Translated from Journal of Tsinghua University (Science and Technology), 2005, 45(10): 1328–1331 (in Chinese) |
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Keywords: | statistic optimization distributed scheduling Bayesian networks data mining |
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