Query-based Learning Particle Swarm Optimization Algorithm for Green Computing
Date Issued
2015
Date
2015
Author(s)
Lin, Shu-Yu
Abstract
Particle swarm optimization (PSO) is one of the most important research topics on swarm intelligence. Existing PSO techniques, however, still contain some significant disadvantages. We present a novel QBL-PSO algorithm that uses QBL (query-based learning) to improve both the exploratory and exploitable capabilities of PSO. Two ways for invoking the QBL are introduced, QP-CR and QP-SR. Here, we apply a QBL method proposed in our previous research to PSO, the new algorithm is first verified through several optimization testing functions. And later on, two green computing applications are introduced and verify the QBL-PSO. The two applications are real cases about power conservation and consumption. The first is power contract problem and the other is virtual machine placement problem in cloud computing. This research not only contributes on improving the PSO through combining QBL, but also advice the PSO-based modules for solving the two green computing applications. Furthermore, QBL not only broadens the diversity of PSO, but also improves its precision. Conventional PSO falls into local solutions when performing queries, instead of finding better global solutions. To overcome the drawbacks, when particles converge in nature, we direct some of them into an ambiguous solution space defined by our algorithm. Our experiment results confirm that the proposed method attains better convergence to the global best solution. We also verify the PSO-based method through solving green computing applications. Both of them successfully reduces energy cost, and to our knowledge, this research presents the first attempt within the literature to apply the QBL concept to PSO.
Subjects
Query-based learning
Particle Swarm optimization
Green Computing
Cloud Computing
Artificial Intelligence
Type
thesis
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