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Self-adaptive bat algorithm for large scale cloud manufacturing service composition

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Abstract

In order to cope with the current economic situation and the trend of global manufacturing, Cloud Manufacturing Mode (CMM) is proposed as a new manufacturing model recently. Massive manufacturing capabilities and resources are provided as manufacturing services in CMM. How to select the appropriate services optimally to complete the manufacturing task is the Manufacturing Service Composition (MSC) problem, which is a key factor in the CMM. Since MSC problem is NP hard, solving large scale MSC problems using traditional methods may be highly unsatisfactory. To overcome this shortcoming, this paper investigates the MSC problem firstly. Then, a Self-Adaptive Bat Algorithm (SABA) is proposed to tackle the MSC problem. In SABA, three different behaviors based on a self-adaptive learning framework, two novel resetting mechanisms including Local and Global resetting are designed respectively to improve the exploration and exploitation abilities of the algorithm for various MSC problems. Finally, the performance of the different flying behaviors and resetting mechanisms of SABA are investigated. The statistical analyses of the experimental results show that the proposed algorithm significantly outperforms PSO, DE and GL25.

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Acknowledgements

This paper was supported by Natural Science Foundation of China (61572262), Natural Science Foundation of Jiangsu Province of China (No. BK20160910, BK20141427), Natural science fund for colleges and universities in Jiangsu Province (No. 16KJB520034), NUPTSF (Grant Nos. NY213047, NY213050, NY214102, NY214098), A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET).

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Authors and Affiliations

  1. School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China

    Bin Xu & Jin Qi

  2. School of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China

    Xiaoxuan Hu & Yanfei Sun

  3. Chinese University of Hong Kong, Shatin, NT, Hong Kong

    Kwong-Sak Leung

  4. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China

    Yu Xue

Authors
  1. Bin Xu
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  2. Jin Qi
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  3. Xiaoxuan Hu
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  5. Yanfei Sun
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  6. Yu Xue
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Correspondence to Bin Xu.

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Guest Editors: Xiaofei Liao, Song Guo, Deze Zeng, and Kun Wang

This article is part of the Topical Collection: Special Issue on Big Data Networking

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Xu, B., Qi, J., Hu, X. et al. Self-adaptive bat algorithm for large scale cloud manufacturing service composition. Peer-to-Peer Netw. Appl. 11, 1115–1128 (2018). https://doi.org/10.1007/s12083-017-0588-y

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