Particle swarm optimization algorithm based on ontology model to support cloud computing applications

Abstract

© 2015, Springer-Verlag Berlin Heidelberg. 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.