Abstract
The particle swarm optimization (PSO) algorithm is a reasonable method for solving complex functions. In previous years, it has been extensively applied in cloud computing environments, such as cloud resource schedules and privacy management. However, this algorithm can easily fall into local minimum points and has a slow convergence speed. Using an established ontology model, we proposed a framework and two novel PSO algorithms in this paper. The ontology model is introduced with various types of operators to the cooperation framework. In contrast with traditional algorithms, our algorithms include semantic roles and concepts to update crucial parameters based on the cooperation framework. Using function optimization problems as examples, the experiments show that the particle swarm algorithms within our framework are superior to other classical algorithms.
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Acknowledgments
The authors declare that there is no conflict of interests regarding the publication of this article. Research was sponsored by the National Science Foundation of China under Grant Number 61272412; Jilin Province Science and Technology Development Plan Item Number 20120303. Soft Science Project of Jiin Provincial Science and Technology Department no. 20150418013fg. Ministry of Education Humanities and Social Sciences Planning Project no. 13YJAZH130, Natural Science Foundation of Jiin Provincial Science and Technology Department no. 20130101074JC. Project 2014095 supported by Graduate Innovation Fund of Jilin University.
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Zhang, C., Yang, Y., Du, Z. et al. Particle swarm optimization algorithm based on ontology model to support cloud computing applications. J Ambient Intell Human Comput 7, 633–638 (2016). https://doi.org/10.1007/s12652-015-0262-2
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DOI: https://doi.org/10.1007/s12652-015-0262-2