麻雀搜索算法在云制造服务组合中的多策略改进
Multi-Strategy Improvement of Sparrow Search Algorithm for Cloud Manufacturing Service Composition
周礼亮 1李犇 2郁清 2戴桂兰 3周国富4
作者信息
- 1. 中国电子科技集团公司第十研究所,四川成都 610000
- 2. 南京邮电大学计算机科学系,江苏南京 210023
- 3. 清华大学信息技术研究院,北京 100084
- 4. 武汉大学计算机学院,湖北武汉 430072
- 折叠
摘要
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.
Abstract
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 insuffi-cient 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 op-timization 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.
关键词
云制造/服务组合优化/服务质量/麻雀搜索算法Key words
cloud manufacturing/service composition optimization/quality of service/sparrow search algorithm引用本文复制引用
基金项目
National Natural Science Foundation of China(62272214)
出版年
2024
NETL
NSTL
万方数据