Advertisement

A hybrid grey wolf optimizer algorithm with evolutionary operators for optimal QoS-aware service composition and optimal selection in cloud manufacturing

  • Hamed Bouzary
  • F. Frank ChenEmail author
ORIGINAL ARTICLE
  • 45 Downloads

Abstract

Cloud manufacturing (CMfg), as a new service-oriented technology, is aiming towards delivering on-demand manufacturing services over the internet by facilitating collaboration among different producers with distributed manufacturing resources and capabilities. To this end, addressing service composition and optimal selection (SCOS) problem has been the pivotal challenge. This NP-hard combinatorial problem deals with selecting and combining the available resources into a composite service to meet the user’s requirements while keeping up the optimal quality of service. This study proposes a new hybrid approach based on the recently developed grey wolf optimizer (GWO) algorithm and evolutionary operators of the genetic algorithm. The embedded crossover and mutation operators carry out a twofold functionality: (1) they make it possible to adapt the continuous structure of GWO to a combinatorial problem such as SCOS, and (2) they help to avoid the local optimal stagnation at the hunting process by providing more exploration strength. A series of experiments were designed and conducted to prove the effectiveness of the proposed algorithm, and the experimental results demonstrated that the proposed algorithm delivers superior performance compared with that of both existing discrete variations of GWO and genetic algorithm, especially in large-scale SCOS problems.

Keywords

Cloud manufacturing Service composition and optimal selection Grey wolf optimizer Metaheuristics Quality of service Industry 4.0 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Krishnaiyer K, Chen FF, Burgess B, Bouzary H (2018) D3S model for sustainable process excellence. Procedia Manufacturing 26:1441–1447.  https://doi.org/10.1016/j.promfg.2018.07.100 CrossRefGoogle Scholar
  2. 2.
    Liu N, Li X (2015) Granulation-based resource classification in cloud manufacturing. Proc Inst Mech Eng B J Eng Manuf 229(7):1258–1270.  https://doi.org/10.1177/0954405415572644 CrossRefGoogle Scholar
  3. 3.
    Zhang Y, Zhang G, Liu Y, Hu D (2017) Research on services encapsulation and virtualization access model of machine for cloud manufacturing. J Intell Manuf 28(5):1109–1123.  https://doi.org/10.1007/s10845-015-1064-2 CrossRefGoogle Scholar
  4. 4.
    Wang T, Guo S, Lee C-G (2014) Manufacturing task semantic modeling and description in cloud manufacturing system. Int J Adv Manuf Technol 71(9):2017–2031.  https://doi.org/10.1007/s00170-014-5607-z CrossRefGoogle Scholar
  5. 5.
    Sahba R, Ebadi N, Jamshidi M, Rad P (2018) Automatic text summarization using customizable fuzzy features and attention on the context and vocabulary. In: 2018 World Automation Congress (WAC), 3–6 June 2018. pp 1–5.  https://doi.org/10.23919/WAC.2018.8430483
  6. 6.
    Ostasevicius V, Jurenas V, Markevicius V, Gaidys R, Zilys M, Cepenas M, Kizauskiene L (2016) Self-powering wireless devices for cloud manufacturing applications. Int J Adv Manuf Technol 83(9):1937–1950.  https://doi.org/10.1007/s00170-015-7617-x CrossRefGoogle Scholar
  7. 7.
    Zhong RY, Lan S, Xu C, Dai Q, Huang GQ (2016) Visualization of RFID-enabled shopfloor logistics big data in cloud manufacturing. Int J Adv Manuf Technol 84(1):5–16.  https://doi.org/10.1007/s00170-015-7702-1 CrossRefGoogle Scholar
  8. 8.
    Zarreh A, Saygin C, Wan H, Lee Y, Bracho A (2018) A game theory based cybersecurity assessment model for advanced manufacturing systems. Procedia Manufacturing 26:1255–1264.  https://doi.org/10.1016/j.promfg.2018.07.162 CrossRefGoogle Scholar
  9. 9.
    Bracho A, Saygin C, Wan H, Lee Y, Zarreh A (2018) A simulation-based platform for assessing the impact of cyber-threats on smart manufacturing systems. Procedia Manufacturing 26:1116–1127.  https://doi.org/10.1016/j.promfg.2018.07.148 CrossRefGoogle Scholar
  10. 10.
    Liang G, Shilong W, Ling K, Yang C (2015) Agent-based manufacturing service discovery method for cloud manufacturing. Int J Adv Manuf Technol 81(9–12):2167–2181.  https://doi.org/10.1007/s00170-015-7221-0 Google Scholar
  11. 11.
    Lv H, Xu Z (2016) Resource matching model of cloud manufacturing platform based on granularity optimization of the SFLA. Revista Tecnica de la Facultad de Ingenieria Universidad del Zulia 39(9):297–307.  https://doi.org/10.21311/001.39.9.38 Google Scholar
  12. 12.
    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):85–101.  https://doi.org/10.1007/s00170-015-8074-2 CrossRefGoogle Scholar
  13. 13.
    Kai Y, Ying C, Fei T (2016) A trust evaluation model towards cloud manufacturing. Int J Adv Manuf Technol 84(1–4):133–146.  https://doi.org/10.1007/s00170-015-8002-5 Google Scholar
  14. 14.
    kulj G, Vrabi R, Butala P, Sluga A (2017) Decentralised network architecture for cloud manufacturing. Int J Comput Integr Manuf 30(4–5):395–408.  https://doi.org/10.1080/0951192X.2015.1066861 Google Scholar
  15. 15.
    Ferreira L, Putnik G, CruzCunha MM, Putnik Z, Castro H, Alves C, Shah V, Varela L (2017) A cloud-based architecture with embedded pragmatics renderer for ubiquitous and cloud manufacturing. Int J Comput Integr Manuf 30(4–5):483–500.  https://doi.org/10.1080/0951192X.2017.1291995 Google Scholar
  16. 16.
    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
  17. 17.
    Li H-F, Zhao L, Zhang B-H, Li J-Q Service matching and composition considering correlations among cloud services. In: Systems, Man, and Cybernetics (SMC), 2015 IEEE International Conference on, 2015. IEEE, pp 509–514Google Scholar
  18. 18.
    Zheng H, Feng Y, Tan J (2016) A fuzzy QoS-aware resource service selection considering design preference in cloud manufacturing system. Int J Adv Manuf Technol 84(1–4):371–379CrossRefGoogle Scholar
  19. 19.
    Wei X, Liu H (2015) A cloud manufacturing resource allocation model based on ant colony optimization algorithm. Int J Grid Distributed Comput 8(1):55–66CrossRefGoogle Scholar
  20. 20.
    Zhou J, Yao X (2017) A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition. Int J Prod Res 55(16):4765–4784CrossRefGoogle Scholar
  21. 21.
    Seghir F, Khababa A (2016) A hybrid approach using genetic and fruit fly optimization algorithms for QoS-aware cloud service composition. J Intell Manuf 29:1773–1792.  https://doi.org/10.1007/s10845-016-1215-0 CrossRefGoogle Scholar
  22. 22.
    Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61.  https://doi.org/10.1016/j.advengsoft.2013.12.007 CrossRefGoogle Scholar
  23. 23.
    Komaki GM, Kayvanfar V (2015) Grey Wolf Optimizer algorithm for the two-stage assembly flow shop scheduling problem with release time. J Comput Sci 8:109–120.  https://doi.org/10.1016/j.jocs.2015.03.011 CrossRefGoogle Scholar
  24. 24.
    Sharma Y, Saikia LC (2015) Automatic generation control of a multi-area ST – thermal power system using Grey Wolf Optimizer algorithm based classical controllers. Int J Electr Power Energy Syst 73:853–862.  https://doi.org/10.1016/j.ijepes.2015.06.005 CrossRefGoogle Scholar
  25. 25.
    Kamboj VK, Bath SK, Dhillon JS (2016) Solution of non-convex economic load dispatch problem using Grey Wolf Optimizer. Neural Comput & Applic 27(5):1301–1316.  https://doi.org/10.1007/s00521-015-1934-8 CrossRefGoogle Scholar
  26. 26.
    Emary E, Zawbaa HM, Hassanien AE (2016) Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371–381.  https://doi.org/10.1016/j.neucom.2015.06.083 CrossRefGoogle Scholar
  27. 27.
    Song X, Tang L, Zhao S, Zhang X, Li L, Huang J, Cai W (2015) Grey Wolf Optimizer for parameter estimation in surface waves. Soil Dyn Earthq Eng 75:147–157.  https://doi.org/10.1016/j.soildyn.2015.04.004 CrossRefGoogle Scholar
  28. 28.
    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 Inform 9(4):2023–2033.  https://doi.org/10.1109/TII.2012.2232936 CrossRefGoogle Scholar
  29. 29.
    Xiang F, Jiang G, Xu L, Wang N (2016) The case-library method for service composition and optimal selection of big manufacturing data in cloud manufacturing system. Int J Adv Manuf Technol 84(1–4):59–70CrossRefGoogle Scholar
  30. 30.
    Zhang W, Yang Y, Zhang S, Yu D, Xu Y (2016) A new manufacturing service selection and composition method using improved flower pollination algorithm. Math Probl Eng 2016:1–12.  https://doi.org/10.1155/2016/7343794 Google Scholar
  31. 31.
    Zhou J, Yao X (2017) Multi-objective hybrid artificial bee colony algorithm enhanced with Lévy flight and self-adaption for cloud manufacturing service composition. Appl Intell 1–22Google Scholar
  32. 32.
    Jin H, Yao X, Chen Y (2015) Correlation-aware QoS modeling and manufacturing cloud service composition. J Intell Manuf 28(8):1947–1960CrossRefGoogle Scholar
  33. 33.
    Liu Y, Xu X, Zhang L, Tao F (2016) An extensible model for multitask-oriented service composition and scheduling in cloud manufacturing. J Comput Inf Sci Eng 16(4):041009CrossRefGoogle Scholar
  34. 34.
    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
  35. 35.
    Liu B, Zhang Z (2017) QoS-aware service composition for cloud manufacturing based on the optimal construction of synergistic elementary service groups. Int J Adv Manuf Technol 88(9–12):2757–2771CrossRefGoogle Scholar
  36. 36.
    Bouzary H, Chen FF (2018) Service optimal selection and composition in cloud manufacturing: a comprehensive survey. Int J Adv Manuf Technol 97(1):795–808.  https://doi.org/10.1007/s00170-018-1910-4 CrossRefGoogle Scholar
  37. 37.
    Bouzary H, Chen FF, Krishnaiyer K (2018) Service matching and selection in cloud manufacturing: a state-of-the-art review. Procedia Manufacturing 26:1128–1136.  https://doi.org/10.1016/j.promfg.2018.07.149 CrossRefGoogle Scholar
  38. 38.
    Zhou J, Yao X (2017) A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition. Int J Adv Manuf Technol 88(9–12):3371–3387CrossRefGoogle Scholar
  39. 39.
    Zhou J, Yao X (2017) Multi-population parallel self-adaptive differential artificial bee colony algorithm with application in large-scale service composition for cloud manufacturing. Appl Soft Comput 56:379–397CrossRefGoogle Scholar
  40. 40.
    Li L, Sun L, Guo J, Qi J, Xu B, Li S (2017) Modified discrete grey wolf optimizer algorithm for multilevel image thresholding. Comput Intell Neurosci 2017Google Scholar
  41. 41.
    Mirjalili S, Lewis A (2013) S-shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm Evol Comput 9:1–14.  https://doi.org/10.1016/j.swevo.2012.09.002 CrossRefGoogle Scholar
  42. 42.
    Wang D, Yang Y, Mi Z (2015) A genetic-based approach to web service composition in geo-distributed cloud environment. Comput Electr Eng 43:129–141.  https://doi.org/10.1016/j.compeleceng.2014.10.008 CrossRefGoogle Scholar
  43. 43.
    Wu Q, Zhu Q, Zhou M (2014) A correlation-driven optimal service selection approach for virtual enterprise establishment. J Intell Manuf 25(6):1441–1453.  https://doi.org/10.1007/s10845-013-0751-0 CrossRefGoogle Scholar
  44. 44.
    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 Inform 4(4):315–327.  https://doi.org/10.1109/TII.2008.2009533 CrossRefGoogle Scholar
  45. 45.
    Chiandussi G, Codegone M, Ferrero S, Varesio FE (2012) Comparison of multi-objective optimization methodologies for engineering applications. Comput Math Appl 63(5):912–942.  https://doi.org/10.1016/j.camwa.2011.11.057 MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringThe University of Texas at San AntonioSan AntonioUSA

Personalised recommendations