Vol.128, No.1, 2021, pp.359-379, doi:10.32604/cmes.2021.015314
A Homogeneous Cloud Task Distribution Method Based on an Improved Leapfrog Algorithm
  • Yunliang Huo1, Ji Xiong1,*, Zhixing Guo1, Qianbing You1, Yi Peng2
1 School of Mechanical Engineering, Sichuan University, Chengdu, 610065, China
2 Chengdu Yigao Intelligent Technology Co., Ltd., Chengdu, 610065, China
* Corresponding Author: Ji Xiong. Email:
(This article belongs to this Special Issue: Intelligent Computing for Engineering Applications)
Received 08 December 2020; Accepted 08 April 2021; Issue published 28 June 2021
Cloud manufacturing is a new manufacturing model with crowd-sourcing characteristics, where a cloud alliance composed of multiple enterprises, completes tasks that a single enterprise cannot accomplish by itself. However, compared with heterogeneous cloud tasks, there are relatively few studies on cloud alliance formation for homogeneous tasks. To bridge this gap, a novel method is presented in this paper. First, a homogeneous cloud task distribution model under cloud environment was constructed, where services description, selection and combination were modeled. An improved leapfrog algorithm for cloud task distribution (ILA-CTD) was designed to solve the proposed model. Different from the current alternatives, the initialization operator and the leapfrog operator in ILA-CTD can ensure that the algorithm always searches the optimal solution in the feasible space. Finally, the processing of task allocation for 1000 pieces of medical labeling machine bottom plates was studied as a case to show the feasibility of the proposed method. The superiority of ILA-CTD was also proven based on more optimal solutions found, compared with the three other methods.
Cloud manufacturing; service composition; tasks distribution; intelligent optimization; leapfrog algorithm
Cite This Article
Huo, Y., Xiong, J., Guo, Z., You, Q., Peng, Y. (2021). A Homogeneous Cloud Task Distribution Method Based on an Improved Leapfrog Algorithm. CMES-Computer Modeling in Engineering & Sciences, 128(1), 359–379.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.