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

作　　者：ZHOU Liliang LI Ben YU Qing DAI Guilan ZHOU Guofu 周礼亮;李犇;郁清;戴桂兰;周国富(中国电子科技集团公司第十研究所,四川成都610000;南京邮电大学计算机科学系,江苏南京210023;清华大学信息技术研究院,北京100084;武汉大学计算机学院,湖北武汉430072)

机构地区：[1]Tenth Research Institute,China Electronics Technology Group Corporation,Chengdu 610000,Sichuan,China [2]Department of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,Jiangsu,China [3]Research Institution of Information Technology,Tsinghua University,Beijing 100084,China [4]School of Computer Science,Wuhan University,Wuhan 430072,Hubei,China

出　　处：《Wuhan University Journal of Natural Sciences》2024年第4期323-337,共15页武汉大学学报（自然科学英文版）

基　　金：Supported by the National Natural Science Foundation of China(62272214)。

摘　　要：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。

关 键 词：cloud manufacturing service composition optimization quality of service sparrow search algorithm 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]