Resource Matching Model of Cloud Manufacturing Platform Based on the Granularity Optimization of the SFLA
Revista Técnica de la Facultad de Ingeniería Universidad del Zulia
For the convergence defects of the shuffleg frog leaping algorithm (SFLA) and needs of the cloud manufacturing platform resources matching, this paper proposes a resource matching model of cloud manufacturing platform based on the granularity optimization of the SFLA. Specifically, Cauchy mutation operator is introduced into the basic SFLA to improve the local update strategy. By improving the random disturbance to expand the search range of the frog, diversity of population is increased. Then,
... is increased. Then, the model uses the adaptive adaptive fitness to optimize the inertia weight of the algorithm, introduces an accelerated contraction factor and adopts the particle swarm optimization algorithm to optimize the SFLA algorithm. Finally, a resource matching model of the cloud manufacturing platform is set up. After the rough selection of data, the improved SFLA is used to subdivide it and match for the model. Simulation results show that performance on the convergence of the original SFLA algorithm is greatly improved by the SFLA optimization stratedy, and in application of building a resource matching model for the cloud manufacturing platform, the improved algorithm has good optimization ability.