基于LG-MMAS算法的制造云服务优化组合研究.pdfVIP

Computer Engineering and Applications 计算机工程与应用 2012 ,48(25) 239 基于LG-MMAS算法的制造云服务优化组合研究 刘志中，薛 霄，安吉宇，鲁保云 LIU Zhizhong , XUE Xiao , AN Jiyu , LU Baoyun 河南理工大学计算机科学与技术学院，河南焦作 454000 College of Computer Sciences and Technology, Henan Polytechnic University, Jiaozuo , Henan 454000 , China LIU Zhizhong, XUE Xiao , AN Jiyu, et al. Research on manufacturing cloud service optimal composition based on CL-MMAS algorithm. Computer Engineering and Applications , 2012 ,48(25) :239-242. Abstract: Manufacturing cIoud service composition is a new technology to improve the utilization and achieve the appreciation of cloud manufacturing resources , and support 出e rapid development of cloud manufacturing indus- tries. As the cloud manufacturing becomes more sophisticated, a large number of manufacturing cloud services with the same founctionalities and different quality of service appear. How to build a composite service through these manufacturing cloud se凹ices that not only can meet user s quality demand, but also has optimal quality is a chal- lenging problem. To solve this problem , this paper introduces the Collaborative Learning (CL) and elite retation mechanism to the Max-Min Ant System (MMAS) , constructs a new optimal algorithm with learning ability, and then applies this algorithm to solve the problem of otpimal manufacturing cloud service composition. The Simula- tion results validate the eff社ctiveness of this algorithm. Key words: cloud manufacturing; service composition; Quality of Service (QoS); Collaborative Leaming (CL); Max-Min Ant System(MMAS) 摘 要:制造云服务组合是一种提高云制造资源利用率，实现制造资源增值的新技术，对云制造产业的快速发 展具有重要的支撑作用。随着云制造技术的日益成熟，网络上出现了大量具有相同制造功能和不同服务质量 的制造云服务，如何通过这些制造云服务构建出既能满足用户制造需求，又具有最优服务质量的组合服务是 云制造领域面临的难题。针对这一问题，将协作学习、变异和精英保留机制引入最大最小蚁群算法，构造了具 有学习和变异能力的最大最小蚁群算法，并使用该算法求解服务质量感知的制造云服务优化组合问题。仿真 实验结果验证了算法的有效性。 关键词:云制造;服务组合;服务质量;协作学习;最大最小蚁群算法 文章编号: 1002-8331 (2012