A dynamic ant-colony genetic algorithm for cloud service composition optimization

  • Yefeng Yang
  • Bo YangEmail author
  • Shilong Wang
  • Feng Liu
  • Yankai Wang
  • Xiao Shu


At present, as the candidate services in the cloud service pool increase, the scale of the service composition increases rapidly. When the existing intelligent optimization algorithms are used to solve the large-scale cloud service composition and optimization (CSCO) problem, it is difficult to ensure the high precision and stability of the optimization results. To overcome such drawbacks, a new dynamic ant-colony genetic hybrid algorithm (DAAGA) is proposed in this paper. The best fusion evaluation strategy is used to determine the invoking timing of genetic and ant-colony algorithms, so the executive time of the two algorithms can be controlled dynamically based on the current solution quality, then the optimization ability is maximized and the overall convergence speed is accelerated. An iterative adjustment threshold is introduced to control the genetic operation and population size in later iterations, in which the effect of genetic algorithm is reduced when the population closes to optimal solution, only the mutation operation is implemented to reduce the calculation, and the population size is increased to find the optimal solution more quickly. A series of comparison experiments are carried out and the results show that the accuracy and stability of DAAGA are significantly improved for the large-scale CSCO problem, and the time consumption of the algorithm is also optimized.


Cloud manufacturing Service composition and optimization Quality of service DAAGA Best fusion point assessment Iterative adjustment threshold 


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The presented work was supported by the Self-Planned Task of State Key Laboratory of Mechanical Transmission (grant number SKLMT-ZZKT-2016Z04), the Fundamental Research Funds for the Central Universities (grant number 106112017CDJXY110001, 2018CDQYJX0013), and the open research fund project of state key laboratory of complex product intelligent manufacturing system technology (grant number QYYE602).

Supplementary material

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Yefeng Yang
    • 1
  • Bo Yang
    • 1
    Email author
  • Shilong Wang
    • 1
  • Feng Liu
    • 2
  • Yankai Wang
    • 1
  • Xiao Shu
    • 1
  1. 1.State Key Laboratory of Mechanical TransmissionChongqing UniversityChongqingChina
  2. 2.State Key Laboratory of Complex Product Intelligent Manufacturing System Technology, CASICBeijingChina

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