Multi-Strategy Improvement of Sparrow Search Algorithm for Cloud Manufacturing Service Composition

In existing research, the optimization of algorithms applied to cloud manufacturing service composition based on the quality of service often suffers from decreased convergence rates and solution quality due to single-population searches in fixed spaces and insufficient information exchange. In this paper, we introduce an improved Sparrow Search Algorithm (ISSA) to address these issues. The fixed solution space is divided into multiple subspaces, allowing for parallel searches that expedite the discovery of target solutions. To enhance search efficiency within these subspaces and significantly improve population diversity, we employ multiple group evolution mechanisms and chaotic perturbation strategies. Furthermore, we incorporate adaptive weights and a global capture strategy based on the golden sine to guide individual discoverers more effectively. Finally, differential Cauchy mutation perturbation is utilized during sparrow position updates to strengthen the algorithm's global optimization capabilities. Simulation experiments on benchmark problems and service composition optimization problems show that the ISSA delivers superior optimization accuracy and convergence stability compared to other methods. These results demonstrate that our approach effectively balances global and local search abilities, leading to enhanced performance in cloud manufacturing service composition.