Advertisement

Peer-to-Peer Networking and Applications

, Volume 11, Issue 5, pp 1115–1128 | Cite as

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

  • Bin XuEmail author
  • Jin Qi
  • Xiaoxuan Hu
  • Kwong-Sak Leung
  • Yanfei Sun
  • Yu Xue
Article
Part of the following topical collections:
  1. Special Issue on Big Data Networking

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.

Keywords

Manufacturing service composition Self-adaptive learning Bat algorithm Dual resetting 

Notes

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).

References

  1. 1.
    Wang K, Shao Y, Shu L, Zhu C, Zhang Y (2016) Mobile big data fault-tolerant processing for ehealth networks. IEEE Netw 30(1):36–42CrossRefGoogle Scholar
  2. 2.
    Xia Z, Wang X, Sun X, Wang Q (2016) A secure and dynamic multi-keyword ranked search scheme over encrypted cloud data. IEEE Trans Parallel Distrib Syst 27(2):340–352CrossRefGoogle Scholar
  3. 3.
    Wang K, Shao Y, Shu L, Han G, Zhu C (2015) LDPA: a local data processing architecture in ambient assisted living communications. IEEE Commun Mag 53(1):56–63CrossRefGoogle Scholar
  4. 4.
    Liu Q, Cai W, Shen J, Fu Z, Liu X, Linge N (2016) A speculative approach to spatial-temporal efficiency with multi-objective optimization in a heterogeneous cloud environment. Secur Commun Netw 9(17):4002–4012CrossRefGoogle Scholar
  5. 5.
    Wang K, Mi J, Xu C, Zhu Q, Shu L, Deng D-J (2016) Real-Time Load Reduction in Multimedia Big Data for Mobile Internet. ACM Trans Multimed Comput Commun Appl 12(5):76Google Scholar
  6. 6.
    Fu Z, Sun X, Liu Q, Zhou L, Shu J (2015) Achieving efficient cloud search services: multi-keyword ranked search over encrypted cloud data supporting parallel computing. IEICE Trans Commun E98B(1):190–200CrossRefGoogle Scholar
  7. 7.
    Wang K, Wang Y, Sun Y, Guo S, Wu J (2016) Green industrial internet of things architecture: an energy-efficient perspective. IEEE Commun Mag 54(12):48–54CrossRefGoogle Scholar
  8. 8.
    Fu Z, Wu X, Guan C, Sun X, Ren K (2016) Toward efficient multi-keyword fuzzy search over encrypted outsourced data with accuracy improvement. IEEE Trans Inf Forensics Secur 11(12):2706–2716CrossRefGoogle Scholar
  9. 9.
    Wang K, Qi X, Shu L, Deng DJ, Rodrigues JJPC (2016) Toward trustworthy crowdsourcing in the social internet of things. IEEE Wirel Commun 23(5):30–36CrossRefGoogle Scholar
  10. 10.
    Wang K, Wang Y, Zeng D, Guo S (2017) An SDN-based architecture for next-generation wireless networks. IEEE Wirel Commun 24(1):25–31CrossRefGoogle Scholar
  11. 11.
    Wang K, Zhuo L, Shao Y, Yue D, Tsang KF (2016) Toward distributed data processing on intelligent leakpoints prediction in petrochemical industries. IEEE Trans Ind Inf 12(6):2091–2102CrossRefGoogle Scholar
  12. 12.
    Wang K, Lu H, Shu L, Rodrigues JJPC (2014) A context-aware system architecture for leak point detection in the large-scale petrochemical industry. IEEE Commun Mag 52(6):62–69CrossRefGoogle Scholar
  13. 13.
    Li B, Zhang L, Wang S, Tao F, Cao J, Jiang X, Song X, Chai X (2010) Cloud manufacturing: a new service-oriented networked manufacturing model. Comput Integr Manuf Syst 16(1):1–7Google Scholar
  14. 14.
    Guo L (2016) A system design method for cloud manufacturing application system. Int J Adv Manuf Technol 84(1–4):275–289CrossRefGoogle Scholar
  15. 15.
    Xu Y, Chen G, Zheng J (2016) An integrated solution-KAGFM for mass customization in customer-oriented product design under cloud manufacturing environment. Int J Adv Manuf Technol 84(1–4):85–101CrossRefGoogle Scholar
  16. 16.
    Liu K, Zhong P, Zeng Q, Li D, Li S (2017) Application modes of cloud manufacturing and program analysis. J Mech Sci Technol 31(1):157–164CrossRefGoogle Scholar
  17. 17.
    Qiu X, He G, Ji X (2016) Cloud manufacturing model in polymer material industry. Int J Adv Manuf Technol 84(1–4):239–248CrossRefGoogle Scholar
  18. 18.
    Tao F, Zuo Y, Xu LD, Zhang L (2014) IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing. IEEE Trans Ind Inf 10(2):1547–1557CrossRefGoogle Scholar
  19. 19.
    Liu I, Jiang H (2012) Research on key technologies for design services collaboration in cloud manufacturing. In proceedings of the 2012 I.E. 16th international conference on computer supported cooperative work in design (CSCWD), pp 824–829Google Scholar
  20. 20.
    Fu C, Xiao M (2014) Optimization method of cloud service composition in cloud manufacturing environment. Appl Res Comput 31(6):1744–1747Google Scholar
  21. 21.
    Tao F, Laili Y, Xu L, Zhang L (2013) FC-PACO-RM: a parallel method for service composition optimal-selection in cloud manufacturing system. IEEE Trans Ind Inf 9(4):2023–2033CrossRefGoogle Scholar
  22. 22.
    Wang H et al (2017) Firefly algorithm with neighborhood attraction. Inf Sci 382:374–387CrossRefGoogle Scholar
  23. 23.
    Wang H, Wang W, Sun H, Rahnamayan S (2016) Firefly algorithm with random attraction. Int J Bio-Inspired Comput 8(1):33–41CrossRefGoogle Scholar
  24. 24.
    Shao Y, Wang K, Shu L, Deng S, Deng D-J (2016) Heuristic optimization for reliable data congestion analytics in crowdsourced eHealth networks. IEEE Access 4:9174–9183CrossRefGoogle Scholar
  25. 25.
    Cai X, Gao X, Xue Y (2016) Improved bat algorithm with optimal forage strategy and random disturbance strategy. Int J Bio-Inspired Comput 8(4):205–214CrossRefGoogle Scholar
  26. 26.
    Xue Y, Jiang J, Zhao B et al (2017) A self-adaptive artificial bee colony algorithm based on global best for global optimization. Soft computing: 1-18Google Scholar
  27. 27.
    Feng J, Kong L (2015) A fuzzy multi-objective genetic algorithm for QoS-based cloud service composition. 2015 11th 2015 11th international conference on semantics, knowledge and grids (skg), pp 202–206Google Scholar
  28. 28.
    Li Y, Yao X, Zhou J (2016) Multi-objective optimization of cloud manufacturing service composition with cloud-entropy enhanced genetic algorithm. Stroj Vestn-J Mech E 62(10):577–590Google Scholar
  29. 29.
    Gupta IK, Kumar J, Rai P (2015) Optimization to quality-of-service-driven web service composition using modified genetic algorithm. 2015 international conference on computer, communication and control (ic4)Google Scholar
  30. 30.
    C. Liu, X. Xiang, C. Zhang, and L. Zheng, A Decision Model for Berth Allocation Under Uncertainty Considering Service Level Using an Adaptive Differential Evolution Algorithm. Asia-Pacific Journal of Operational Research, 33(6):1650049, Dec. 2016Google Scholar
  31. 31.
    Zhou Y, Zhang C, Zhang B (2015) Multi-objective service composition optimization using differential evolution. 2015 11th international conference on natural computation (icnc), pp 233–238Google Scholar
  32. 32.
    Hossain MS, Moniruzzaman M, Muhammad G, Ghoneim A, Alamri A (2016) Big data-driven service composition using parallel clustered particle swarm optimization in mobile environment. IEEE Trans Serv Comput 9(5):806–817CrossRefGoogle Scholar
  33. 33.
    Tao F, Zhao D, Hu Y, Zhou Z (2008) Resource service composition and its optimal-selection based on particle swarm optimization in manufacturing grid system. IEEE Trans Ind Inf 4(4):315–327CrossRefGoogle Scholar
  34. 34.
    X.-S. Yang, A New Metaheuristic Bat-Inspired Algorithm. In Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), J. R. González, D. A. pelta, C. Cruz, G. Terrazas, and N. Krasnogor, Eds. Springer, Berlin Heidelberg, pp 65–74, 2010Google Scholar
  35. 35.
    Nakamura RYM, Pereira LAM, Costa KA, Rodrigues D, Papa JP, Yang XS (2012) BBA: A Binary Bat Algorithm for Feature Selection. In 2012 25th SIBGRAPI conference on graphics, Patterns and Images, pp 291–297Google Scholar
  36. 36.
    Senthilnath J, Kulkarni S, Benediktsson JA, Yang XS (2016) A novel approach for multispectral satellite image classification based on the bat algorithm. IEEE Geosci Remote Sens Lett 13(4):599–603CrossRefGoogle Scholar
  37. 37.
    García-Martínez C, Lozano M, Herrera F, Molina D, Sánchez AM (2008) Global and local real-coded genetic algorithms based on parent-centric crossover operators. Eur J Oper Res 185(3):1088–1113CrossRefzbMATHGoogle Scholar
  38. 38.
    Xiang F, Hu Y, Yu Y, Wu H (2013) QoS and energy consumption aware service composition and optimal-selection based on Pareto group leader algorithm in cloud manufacturing system. CEJOR 22(4):663–685CrossRefzbMATHGoogle Scholar
  39. 39.
    Lartigau J, Xu X, Nie L, Zhan D (2015) Cloud manufacturing service composition based on QoS with geo-perspective transportation using an improved artificial bee Colony optimisation algorithm. Int J Prod Res 53(14):4380–4404CrossRefGoogle Scholar
  40. 40.
    Liu W, Liu B, Sun D, Li Y, Ma G (2013) Study on multi-task oriented services composition and optimisation with the ‘multi-composition for each task’ pattern in cloud manufacturing systems. Int J Comput Integr Manuf 26(8):786–805CrossRefGoogle Scholar
  41. 41.
    Shengyu Pei, Aijia Ouyang, and Lang Tong (2015) A Hybrid Algorithm Based on Bat-Inspired Algorithm and Differential Evolution for Constrained Optimization Problems. Int  J  Patt  Recogn  Artif  Intell  29: 1559007 Google Scholar
  42. 42.
    Khooban MH, Niknam T (2015) A new intelligent online fuzzy tuning approach for multi-area load frequency control: self adaptive modified bat algorithm. Int J Electr Power Energy Syst 71:254–261CrossRefGoogle Scholar
  43. 43.
    Yang X-S (2012) Bat algorithm for multi-objective optimisation. Int J Bio-Inspired Comput 3(5):267–274CrossRefGoogle Scholar
  44. 44.
    Tao F, Hu Y, Zhao D, Zhou Z, Zhang H, Lei Z (2008) Study on manufacturing grid resource service QoS modeling and evaluation. Int J Adv Manuf Technol 41(9–10):1034–1042Google Scholar
  45. 45.
    Tao F, Hu YF, Zhou ZD (2009) Application and modeling of resource service trust-QoS evaluation in manufacturing grid system. Int J Prod Res 47(6):1521–1550CrossRefGoogle Scholar
  46. 46.
    J. Montgomery, Stephen Chen (2014) Standard Particle Swarm Optimization on the CEC2013 Real-Parameter Optimization Benchmark Functions -- revisedGoogle Scholar
  47. 47.
    Mark H, Martin P (2011) An introduction and survey of estimation of distribution algorithms. Swarm and Evolutionary Computation 1(93):111–128Google Scholar
  48. 48.
    Hansen N, Ostermeier A (2001) Completely Derandomized self-adaptation in evolution strategies. Evol Comput 9(2):159–195CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.School of Internet of ThingsNanjing University of Posts and TelecommunicationsNanjingChina
  2. 2.School of AutomationNanjing University of Posts and TelecommunicationsNanjingChina
  3. 3.Chinese University of Hong KongShatinHong Kong
  4. 4.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina

Personalised recommendations