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A self-learning bee colony and genetic algorithm hybrid for cloud manufacturing services

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Abstract

At present, cloud services and cloud manufacturing are developing rapidly, speed and accuracy have become the themes of cloud manufacturing development. The core component of cloud manufacturing is resource portfolio optimization. In cloud manufacturing today, the scale of service portfolios is expanding rapidly, and the number of candidate services which in the service pool is increasing gradually. To adapt to the development of cloud services, an optimization algorithm with faster speed, greater precision and higher stability is required to solve the problem of cloud service composition and optimization (CSCO). To increase the convergence rate and avoid falling into local optima with the artificial bee colony algorithm, a self-learning artificial bee colony genetic algorithm (SLABC-GA) is proposed in this paper, which is based on reinforcement learning (RL), and the RL is used to intelligently select the number of dimensions of each update of a feasible solution. A global optimal individual is used to search and guide the search equation to avoid obtaining local optima and improve algorithm development and the precision of the traditional artificial bee colony algorithm (ABC). A genetic algorithm (GA) is introduced in a later stage of the algorithm to further improve its accuracy and convergence speed. Additionally, this paper analyzes and constructs the self-learning model in SLABC, the optimal learning method is Q-learning algorithm, and designs a reward method and state determination method of RL in the environment of the bee colony algorithm. Finally, a large number of comparative experiments have been carried out, the results show that the accuracy and speed of the SLABC-GA outperform for CSCO problems, and the performance of the SLABC-GA for large-scale CSCO problems is better than that of genetic algorithm (GA) and the traditional artificial bee colony algorithm (ABC).

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Funding

This study was carried out with the support of the China Postdoctoral Science Foundation (2019M662410) and the Key Technologies Research and Development Program (2018AAA0101804).

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Authors and Affiliations

  1. College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an, 271018, China

    Tianhua Li, Yongcheng Yin, Jialin Hou & Kai Zhou

  2. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044, China

    Bo Yang

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  1. Tianhua Li
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Contributions

The theoretical model was constructed by KZ and BY. TL and JH designed the experiment. YY developed the MATLAB program. The data analyses were performed by KZ, YY and TL. The original draft, review, and editing were writen by YY.

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Correspondence to Kai Zhou.

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Li, T., Yin, Y., Yang, B. et al. A self-learning bee colony and genetic algorithm hybrid for cloud manufacturing services. Computing 104, 1977–2003 (2022). https://doi.org/10.1007/s00607-022-01079-0

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