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

麻雀搜索算法在云制造服务组合中的多策略改进

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.

在现有研究中,基于服务质量的云制造服务组合算法优化常常由于在固定空间中进行单一种群搜索和信息交换不足,导致收敛速度和解质量下降。本文提出了一种改进的麻雀搜索算法(ISSA)来解决这些问题。将固定的解空间划分为多个子空间,并使用并行搜索,从而加快目标解的发现速度。为了提高这些子空间内的搜索效率并显著改善种群多样性,采用了多组进化机制和混沌扰动策略。此外,结合了基于黄金正弦的自适应权重和全局捕捉策略,更有效地引导个体发现者。最后,在麻雀位置更新过程中使用差分柯西变异扰动,以增强算法的全局优化能力。在基准问题和服务组合优化问题上的模拟实验表明,ISSA在优化精度和收敛稳定性方面优于其他方法。结果表明,本文的方法有效平衡了全局搜索和局部搜索能力,从而在云制造服务组合中表现出更佳的性能。