An Improved Grey Wolf Optimizer Algorithm for Energy-Aware Service Composition in Cloud Manufacturing

  • Yefeng Yang
  • Bo YangEmail author
  • Shilong Wang
  • Wei Liu
  • Tianguo Jin


Green and sustainable manufacturing is an inevitable development trend of manufacturing in the future, wherein low-energy consumption is regarded as a significant component. As an emerging manufacturing service system, cloud manufacturing (CMfg) is characterized by wide distribution, large quantity and complicated calling method for manufacturing services. Therefore, the energy consumption in the execution of composition service should be considered. However, few works related to energy consumption are focused on service composition at present. In response, this paper proposes an energy-aware service composition and optimal selection (EA-SCOS) model to ensure high-quality and low-energy consumption during the tasks. The mathematical model of energy consumption based on quality of service (QoS) is established, wherein the evaluation methods for manufacturing energy consumption and logistics energy consumption are described in detail. In order to solve the EA-SCOS problem effectively, a state-of-the-art algorithm which has been successfully applied in other fields, named grey wolf optimizer (GWO), is introduced. In addition, two key improvements for GWO are proposed to ensure the accuracy of solution. Finally, the effectiveness and feasibility of improved GWO (IGWO) are verified by a comparative study with GWO, genetic algorithm (GA) and max–min ant system (MMAS). Simulation results show that the proposed model is valid for reducing service energy consumption. Moreover, the improved strategies have obvious, positive effect on the accuracy of solutions and the performance of IGWO for addressing EA-SCOS problem is obviously better than the other three algorithms.


Cloud manufacturing Service composition Energy consumption Grey wolf optimizer 


Funding information

The presented work was supported by the Key Technologies Research and Development Program of China (no. 2018AAA0101804), Fundamental Research Funds for the Central Universities (2018CDQYJX0013), the National Defense Basic Research Project of China (no. JCKY2016204A502), and the open research fund project of state key laboratory of complex product intelligent manufacturing system technology (grant number QYYE602).

Supplementary material

170_2019_4449_MOESM1_ESM.opju (529 kb)
ESM 1 (OPJU 528 kb)
170_2019_4449_MOESM2_ESM.xlsx (40 kb)
ESM 2 (XLSX 30 kb)
170_2019_4449_MOESM3_ESM.xlsx (31 kb)
ESM 3 (XLSX 39 kb)


  1. 1.
    Zhang L, Luo YL, Tao F, Li BH, Ren L, Zhang XS, Guo H, Cheng Y, Hu AR, Liu YK (2014) Cloud manufacturing: a new manufacturing paradigm. Enterp Inf Syst 8(2):167–187. CrossRefGoogle Scholar
  2. 2.
    Tao F, Cheng Y, Xu LD, Zhang L, Li BH (2014) CCIoT-CMfg: Cloud computing and Internet of Things-based cloud manufacturing service system. IEEE Trans Ind Inform 10(2):1435–1442. CrossRefGoogle Scholar
  3. 3.
    He W, Xu L (2015) A state-of-the-art survey of cloud manufacturing. Int J Comput Integr Manuf 28(3):239–250. CrossRefGoogle Scholar
  4. 4.
    Ren L, Zhang L, Wang L, Tao F, Chai X (2017) Cloud manufacturing: key characteristics and applications. Int J Comput Inter Manuf 30(6):501–515. CrossRefGoogle Scholar
  5. 5.
    Akbaripour H, Houshmand M, Van Woensel T, Mutlu N (2017) Cloud manufacturing service selection optimization and scheduling with transportation considerations: mixed-integer programming models. Int J Adv Manuf Technol 95(1-4):43–70. CrossRefGoogle Scholar
  6. 6.
    Garg S, Modi K, Chaudhary S (2016) A QoS-aware approach for runtime discovery, selection and composition of semantic web services. Int J Web Inf Syst 12(2):177–200. CrossRefGoogle Scholar
  7. 7.
    Liu ZZ, Xue X, Shen JQ, Li WR (2013) Web service dynamic composition based on decomposition of global QoS constraints. Int J Adv Manuf Technol 69(9-12):2247–2260. CrossRefGoogle Scholar
  8. 8.
    Hayyolalam V, Kazem AAP (2018) A systematic literature review on QoS-aware service composition and selection in cloud environment. J Netw Comput Appl 110:52–74. CrossRefGoogle Scholar
  9. 9.
    Liu ZZ, Song C, Chu DH, Hou ZW, Peng WP (2017) An approach for multipath cloud manufacturing services dynamic composition. Int J Intell Syst 32(4):371–393. CrossRefGoogle Scholar
  10. 10.
    Cao Y, Wang SL, Kang L, Gao Y (2016) A TQCS-based service selection and scheduling strategy in cloud manufacturing. Int J Adv Manuf Technol 82(1-4):235–231. CrossRefGoogle Scholar
  11. 11.
    Lu Y, Xu X (2017) A semantic web-based framework for service composition in a cloud manufacturing environment. J Manuf Syst 42:69–81. CrossRefGoogle Scholar
  12. 12.
    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 16:041009. CrossRefGoogle Scholar
  13. 13.
    Wang L, Guo S, Li X, Du B, Xu W (2016) Distributed manufacturing resource selection strategy in cloud manufacturing. Int J Adv Manuf Technol 94(9-12):3375–3388. CrossRefGoogle Scholar
  14. 14.
    Xiang F, Hu Y, Yu Y, Wu H (2014) Qos and energy consumption aware service composition and optimal-selection based on pareto group leader algorithm in cloud manufacturing system. Cent Eur J Oper Res 22(4):663–685. CrossRefzbMATHGoogle Scholar
  15. 15.
    Tao F, Feng Y, Zhang L, Liao TW (2014) CLPS-GA: a case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling. Appl Soft Comput 19(6):264–279. CrossRefGoogle Scholar
  16. 16.
    Jia G, Han G, Jiang J, Liu L (2017) Dynamic adaptive replacement policy in shared last-level cache of DRAM/PCM hybrid memory for big data storage. IEEE Trans Ind Inform 13(4):1951–1960. CrossRefGoogle Scholar
  17. 17.
    Zheng H, Feng Y, Tan J (2017) A hybrid energy-aware resource allocation approach in cloud manufacturing environment. IEEE Access 5:12648–12656. CrossRefGoogle Scholar
  18. 18.
    Laili Y, Tao F, Zhang L, Sarker BR (2012) A study of optimal allocation of computing resources in cloud manufacturing systems. Int J Adv Manuf Technol 63(5-8):671–690. CrossRefGoogle Scholar
  19. 19.
    Jin H, Yao X, Chen Y (2017) Correlation-aware QoS modeling and manufacturing cloud service composition. J Intell Manuf 28(8):1947–1960. CrossRefGoogle Scholar
  20. 20.
    Wu QW, Zhu QS, Zhou MQ (2014) A correlation-driven optimal service selection approach for virtual enterprise establishment. J Intell Manuf 25(6):1441–1453. CrossRefGoogle Scholar
  21. 21.
    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 56:379–397. CrossRefGoogle Scholar
  22. 22.
    Liu J, Chen YL, Wang L, Zuo LD, Niu YF (2018) An approach for service composition optimisation considering service correlation via a parallel max–min ant system based on the case library. Int J Comput Integr Manuf 31(12):1174–1188. CrossRefGoogle Scholar
  23. 23.
    Zhang WY, Yang YS, Zhang S, Yu DJ, Li YC (2018) Correlation-aware manufacturing service composition model using an extended flower pollination algorithm. Int J Prod Res 56(14):4676–4691. CrossRefGoogle Scholar
  24. 24.
    Zhang WY, Yang YS, Zhang S, Yu DJ, Xu YB (2016) A new manufacturing service selection and composition method using improved flower pollination algorithm. Math Probl Eng. Google Scholar
  25. 25.
    Cao Y, Wang S, Kang L, Li C, Guo L (2015) Study on machining service modes and resource selection strategies in cloud manufacturing. Int J Adv Manuf Technol 81(1-4):597–613. CrossRefGoogle Scholar
  26. 26.
    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):371–379. CrossRefGoogle Scholar
  27. 27.
    Huang BQ, Li CH, Tao F (2014) A chaos control optimal algorithm for QoS-based service composition selection in cloud manufacturing system. Enterp Inf Syst 8(4):445–463. CrossRefGoogle Scholar
  28. 28.
    Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69(3):46–61. CrossRefGoogle Scholar
  29. 29.
    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. CrossRefGoogle Scholar
  30. 30.
    Song XH, Tang L, Zhao ST, Zhang XQ, Li L, Huang JQ, Cai W (2015) Grey wolf optimizer for parameter estimation in surface waves. Soil Dyn Earthq Eng 75:147–157. CrossRefGoogle Scholar
  31. 31.
    Medjahed SA, Saadi TA, Benyettou A, Ouali M (2016) Gray wolf optimizer for hyperspectral band selection. Appl Soft Comput 40:178–186. CrossRefGoogle Scholar
  32. 32.
    Bouzary H, Chen FF (2018) Service optimal selection and composition in cloud manufacturing: a comprehensive survey. Int J Adv Manuf Technol 97(1-4):795–808. CrossRefGoogle Scholar
  33. 33.
    Zhang Y, Tao F, Laili Y, Hou B, Lv L, Zhang L (2013) Green partner selection in virtual enterprise based on Pareto genetic algorithms. Int J Adv Manuf Technol 67(9-12):2109–2125. CrossRefGoogle Scholar
  34. 34.
    Zuo Y, Tao F, Nee NYC (2018) An Internet of things and cloud-based approach for energy consumption evaluation and analysis for a product. Int J Comput Integr Manuf 31(4-5):337–348. CrossRefGoogle Scholar
  35. 35.
    Fisher O, Watson N, Porcu L, Bacon D, Rigley M, Gomes RL (2018) Cloud manufacturing as a sustainable process manufacturing route. J Manuf Syst 47:53–68. CrossRefGoogle Scholar
  36. 36.
    Xiao F, Hu ZH, Wang KX, Fu PH (2015) Spatial distribution of energy consumption and carbon emission of regional logistics. Sustainability 7:9140–9159. CrossRefGoogle Scholar
  37. 37.
    Wu QW, Ishikawa F, Zhu QS, Shin DH (2016) QoS-aware multigranularity service composition: modeling and optimization. IEEE Trans Syst Man Cybern-Syst 46(11):1565–1577. CrossRefGoogle Scholar
  38. 38.
    Zeng LZ, Benatallah B, Ngu AHH, Dumas M, Kalagnanam J, Chang H (2004) QoS-aware middleware for web services composition. IEEE Trans Softw Eng 30(5):311–327. CrossRefGoogle Scholar
  39. 39.
    Long W, Jiao JJ, Liang XM, Tang MZ (2018) Inspired grey wolf optimizer for solving large-scale function optimization problems. Appl Math Model 60:112–126. MathSciNetCrossRefGoogle Scholar
  40. 40.
    Yang YF, Yang B, Wang SL, Liu F, Wang YK, Shu X (2019) A dynamic ant-colony genetic algorithm for cloud service composition optimization. Int J Adv Manuf Technol 102(1-4):355–368. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Yefeng Yang
    • 1
  • Bo Yang
    • 1
    Email author
  • Shilong Wang
    • 1
  • Wei Liu
    • 2
  • Tianguo Jin
    • 3
  1. 1.State Key Laboratory of Mechanical TransmissionChongqing UniversityChongqingChina
  2. 2.State Key Laboratory of Complex Product Intelligent Manufacturing System TechnologyCASICBeijingChina
  3. 3.School of Mechatronics EngineeringHarbin Institute of TechnologyHarbinChina

Personalised recommendations