Cloud manufacturing resource optimization allocation method based on improved sparrow search algorithm

Cloud manufacturing was a new type of manufacturing model, and a cloud task decomposition method and a service composition optimization method were proposed to address the problem of cloud manufacturing resource optimization allocation. Firstly, a correlation matrix was established based on the information correlation between atomic tasks, then hierarchical clustering algorithm was used to reorganize the atomic tasks, and finally the decomposition of the total tasks was realized. For the service composition problem of cloud manufacturing, an improved sparrow search algorithm (ISSA) was proposed. The ISSA adopted the Latin hypercube sampling method to generate an initialized population and incorporated the golden sine operator and the opposition-based learning strategy to avoid the algorithm from falling into a local optimum. Finally, an example of Bluetooth headset manufacturing was used to validate the resource optimization allocation method proposed in this paper, which demonstrated the feasibility of the method proposed.