Parallel manufacturing cloud service composition algorithm based on collaborative effect

被引：0
|
作者：
Chen Y. [1 ]
Liu J. [1 ]
Ling L. [1 ]
Wang L. [1 ]
机构：
[1] State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2019年 / 25卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Ant colony algorithm; Cloud manufacturing; Collaborative effect; Parallel service composition; Quality of service;
D O I：
10.13196/j.cims.2019.01.013
中图分类号：
学科分类号：
摘要：
To solve the problem of manufacturing cloud service composition optimization in parallel structure, an improved ant colony algorithm based on reverse-learning and local-learning was proposed from the perspective of collaborative effect. An evaluation model was established to calculate the value of collaborative effect as the heuristic function parameter, and the quality of service as the pheromone. Then, the collaborative effect of service composition calculated by collaborative relationship matrix was combined with the quality of service composition to obtain the optimal service composition. The experimental results showed that the improved algorithm was effective and feasible, which could achieve the global optimal solution quickly. © 2019, Editorial Department of CIMS. All right reserved.
引用
收藏
页码：137 / 146
页数：9
相关论文
共 19 条
  • [1] Li B., Zhang L., Wang S., Et al., Cloud manufacturing: a new service-oriented networked manufacturing model, Computer Integrated Manufacturing Systems, 16, 1, pp. 1-8, (2010)
  • [2] Li B., Zhang L., Ren L., Et al., Further discussion cloud manufacturing, Computer Integrated Manufacturing Systems, 17, 3, pp. 449-457, (2010)
  • [3] Yin C., Huang B., Liu F., Et al., Common key technology system of cloud manufacturing service platform for small and medium enterprises, Computer Integrated Manufacturing Systems, 17, 3, pp. 495-503, (2011)
  • [4] Zhang L., Luo Y., Tao F., Et al., Study on the key technologies for the construction of manufacturing cloud, Computer Integrated Manufacturing Systems, 16, 11, pp. 2510-2520, (2010)
  • [5] Tao F., Zhang L., Guo H., Et al., Typical characteristics of cloud manufacturing and several key issues of cloud service composition, Computer Integrated Manufacturing Systems, 17, 3, pp. 477-486, (2011)
  • [6] Liu Z.Z., Chu D.H., Song C., Et al., Social learning optimization algorithm paradigm and its application in QoS-aware cloud service composition, Information Sciences, 326, C, pp. 315-333, (2016)
  • [7] Strunk A., QoS-aware service composition: a survey, Proceedings of the 8th IEEE European Conference on Web Service, pp. 67-74, (2010)
  • [8] Xue X., Wang S., Lu B., Manufacturing service composition method based on networked collaboration mode, Journal of Network and Computer Applications, 59, C, pp. 28-38, (2016)
  • [9] Dong Y., Guo G., Evalution and selection approach for cloud manufacturing service based on template and global trust degree, Computer Integrated Manufacturing Systems, 20, 1, pp. 207-214, (2014)
  • [10] Tai L., Hu R., Zhao H., Et al., Multi-objective dynamic scheduling of manufacturing resource to cloud manufacturing services, China Mechanical Engineering, 24, 12, pp. 1616-1622, (2013)
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