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A niching behaviour-based algorithm for multi-level manufacturing service composition optimal-selection

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Abstract

To improve the accuracy of service modelling and optimal selection in cloud manufacturing (CMfg), a multi-level modelling methodology is proposed to describe manufacturing services. In this methodology, manufacturing services are divided into three levels: resource, functional and process services. Based on time, cost and reputation analysis of these three service levels, the corresponding objective functions and services composition constraints are established. Considering intelligent optimal selection, a niching behaviour-based gravitational search algorithm (NGSA) is designed to address manufacturing service composition and optimal selection (MSCOS) problems. In NGSA, the niche crowding factor and fitness sharing technology are introduced to the standard gravitational search algorithm (GSA) to improve its convergence speed and accuracy. The results of a simulation experiment demonstrate that the proposed algorithm can find better solutions in less time than previous algorithms, such as the adaptive genetic algorithm (AGA) and the modified particle swarm optimization (MPSO) algorithm.

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Acknowledgements

This research is supported by the National Science and Technology Major Project of China under Grant No. 2018ZX04001006. The authors would like to thank the editor and the anonymous reviewers for their constructive and helpful comments, which helped to improve the presentation of the paper.

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Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, 100191, People’s Republic of China

    Tao Ding, Guangrong Yan, Yi Lei & Xiangyu Xu

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  1. Tao Ding
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  2. Guangrong Yan
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  3. Yi Lei
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  4. Xiangyu Xu
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Correspondence to Guangrong Yan.

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Ding, T., Yan, G., Lei, Y. et al. A niching behaviour-based algorithm for multi-level manufacturing service composition optimal-selection. J Ambient Intell Human Comput 11, 1177–1189 (2020). https://doi.org/10.1007/s12652-019-01250-0

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