Advertisement

Improved adaptive immune genetic algorithm for optimal QoS-aware service composition selection in cloud manufacturing

  • Yi Que
  • Wei Zhong
  • Hailin Chen
  • Xinan Chen
  • Xu Ji
ORIGINAL ARTICLE

Abstract

Developments in new information technology have indicated that single manufacturing services are now unable to satisfy users’ multi-objective demands, especially in the process industry. As a new user-centric, service-oriented, demand-driven manufacturing model, cloud manufacturing can provide high-reliability, low-cost, fast-time, high-ability services. This study presents a new Manufacturers to Users (M2U) mode for cloud manufacturing, aiming at solving the core manufacturing service composition optimal selection (MSCOS) problem. The M2U mode expands the service areas and improves its dynamic optimal allocation capabilities of resources by efficient and flexible management and operation of services. Firstly, a comprehensive mathematical evaluation model with four critical quality of service (QoS)-aware indexes (time, reliability, cost, and ability) is constructed. Secondly, a new information entropy immune genetic algorithm (IEIGA) is proposed for the model solution. Finally, nine MSCOS problems of different scales are illustrated so as to compare the performance of the three algorithms. The results prove the effectiveness and superiority of the proposed algorithm and its suitability for solving large-scale service composition problems.

Keywords

Cloud manufacturing Quality of service Service composition Manufacturers to users Immune genetic algorithm 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

The authors would like to thank Li Zhou and Yu Du for their constructive suggestions on the improvements of the service model, and gratefully acknowledge the support of the National Natural Science Foundation, China (No. 51473102, No. 21776183).

Supplementary material

170_2018_1925_MOESM1_ESM.doc (520 kb)
ESM 1 (DOC 520 kb)

References

  1. 1.
    Tao F, Zhang L, Venkatesh VC, Luo Y, Cheng Y (2011) Cloud manufacturing: a computing and service-oriented manufacturing model. Proc Inst Mech Eng B J Eng 225(10):1969–1976CrossRefGoogle Scholar
  2. 2.
    Tao F, Cheng Y, Zhang L, Nee AYC (2017) Advanced manufacturing systems: socialization characteristics and trends. J Intell Manuf 28(5):1079–1094CrossRefGoogle Scholar
  3. 3.
    Tao F, Zhang L, Liu YK, Cheng Y, Wang LH, Xu X (2015) Manufacturing service management in cloud manufacturing: overview and future research directions. J Manuf Sci Eng Trans ASME 137(4):040912CrossRefGoogle Scholar
  4. 4.
    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):167–187CrossRefGoogle Scholar
  5. 5.
    Ren L, Zhang L, Wang LH, Tao F, Chai XD (2017) Cloud manufacturing: key characteristics and applications. Int J Comput Integr Manuf 30(6):501–515CrossRefGoogle Scholar
  6. 6.
    Tao F, Zhao DM, Hu Y, Zhou Z (2008) Resource service composition and its optimal-selection based on particle swarm optimization in manufacturing grid system. IEEE Trans Industr Inform 4(4):315–327CrossRefGoogle Scholar
  7. 7.
    Huang XR, Du BG, Sun LB, Chen F, Dai W (2016) Service requirement conflict resolution based on ant colony optimization in group-enterprises-oriented cloud manufacturing. Int J Adv Manuf Technol 84(1–4):183–196CrossRefGoogle Scholar
  8. 8.
    Hu YJ, Chang XF, Wang Y, Wang ZL, Shi C, Wu LZ (2016) Cloud manufacturing resources fuzzy classification based on genetic simulated annealing algorithm. Mater Manuf Process 32(10):1109–1115CrossRefGoogle Scholar
  9. 9.
    Lartigau J, Xu XF, Nie LS, Zhan DC (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
  10. 10.
    Fan XQ (2013) A decision-making method for personalized composite service. Expert Syst Appl 40(15):5804–5810CrossRefGoogle Scholar
  11. 11.
    Zheng H, Feng YX, Tan JR (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
  12. 12.
    Wang DD, Yang Y, Ming ZQ (2015) A genetic-based approach to web service composition in geo-distributed cloud environment. Comput Electr Eng 43:129–141CrossRefGoogle Scholar
  13. 13.
    Wang D, Shao XD, Liu SM (2017) Assembly sequence planning for reflector panels based on genetic algorithm and ant Colony optimization. Int J Adv Manuf Technol 91(1–4):987–997CrossRefGoogle Scholar
  14. 14.
    Lin YK, Chong CS (2017) Fast GA-based project scheduling for computing resources allocation in a cloud manufacturing system. J Intell Manuf 28(5):1189–1201MathSciNetCrossRefGoogle Scholar
  15. 15.
    Keane AJ (1995) Genetic algorithm optimization of multi-peak problems: studies in convergence and robustness. Artif Intell Eng 9(2):75–83CrossRefGoogle Scholar
  16. 16.
    Jiang H, Yi JJ, Chen SL, Zhu XM (2016) A multi-objective algorithm for task scheduling and resource allocation in cloud-based disassembly. J Manuf Syst 41:239–255CrossRefGoogle Scholar
  17. 17.
    Horton P, Jaboyedoff M, Obled C (2017) Global optimization of an analog method by means of genetic algorithms. Mon Weather Rev 145(4):1275–1294CrossRefGoogle Scholar
  18. 18.
    Okada I, Takahashi K, Zhang WQ, Zhang XF, Yang HY, Fujimura SR (2014) A genetic algorithm with local search using activity list characteristics for solving resource-constrained project scheduling problem with multiple modes. IEEJ Trans Electr Electron Eng 9(2):190–199CrossRefGoogle Scholar
  19. 19.
    Srinivas M, Patnaik LM (2002) Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cybern 24(4):656–667CrossRefGoogle Scholar
  20. 20.
    Luo QK, Wu JF, Sun XM, Yang Y, Wu JC (2012) Optimal design of groundwater remediation systems using a multi-objective fast harmony search algorithm. Hydrogeol J 20(8):1497–1510CrossRefGoogle Scholar
  21. 21.
    Ye ZS, Li ZZ, Xie M (2010) Some improvements on adaptive genetic algorithms for reliability-related applications. Reliab Eng Syst Saf 95(2):120–126CrossRefGoogle Scholar
  22. 22.
    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):445–463CrossRefGoogle Scholar
  23. 23.
    Zinflou A, Gagné C, Gravel M (2012) GISMOO: a new hybrid genetic/immune strategy for multiple-objective optimization. Comput Oper Res 39(9):1951–1968MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Xu XD, Li CX (2007) Research on immune genetic algorithm for solving the job-shop scheduling problem. Int J Adv Manuf Technol 34(7–8):783–789CrossRefGoogle Scholar
  25. 25.
    Li ZF, Gu JF, Zhuang HY, Kang L, Zhao XY, Guo Q (2015) Adaptive molecular docking method based on information entropy genetic algorithm. Appl Soft Comput 26:299–302CrossRefGoogle Scholar
  26. 26.
    Xiang F, Hu YF, Yu YR, Wu HC (2014) Qos and energy consumption aware service composition and optimal-selection based on pareto group leader algorithm in cloud manufacturing system. Cent Europ J Oper Res 22(4):663–685CrossRefzbMATHGoogle Scholar
  27. 27.
    Zhou JJ, Yao XF (2017) De-caabc: differential evolution enhanced context-aware artificial bee colony algorithm for service composition and optimal selection in cloud manufacturing. Int J Adv Manuf Technol 90(1–4):1085–1103CrossRefGoogle Scholar
  28. 28.
    Liu ZZ, Song C, Chu DH, Hou ZW, Peng WP (2017) An approach for multipath cloud manufacturing services dynamic composition. Int J Intell Syst 32:371–393CrossRefGoogle Scholar
  29. 29.
    Chen FZ, Dou RL, Li MQ, Wu H (2016) A flexible qos-aware web service composition method by multi-objective optimization in cloud manufacturing. Comput Ind Eng 99:423–431CrossRefGoogle Scholar
  30. 30.
    Zhou JJ, Yao XF (2017) Hybrid teaching–learning-based optimization of correlation-aware service composition in cloud manufacturing. Int J Adv Manuf Technol 91(9–12):3515–3533CrossRefGoogle Scholar
  31. 31.
    Tao F, Laili YJ, Xu LD, Zhang L (2013) FC-PACO-RM: a parallel method for service composition optimal-selection in cloud manufacturing system. IEEE Trans Ind Inform 9(4):2023–2033CrossRefGoogle Scholar
  32. 32.
    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):2247–2260CrossRefGoogle Scholar
  33. 33.
    Xu WJ, Tian SS, Liu Q, Xie YQ, Zhou ZD, Pham DT (2016) An improved discrete bees algorithm for correlation-aware service aggregation optimization in cloud manufacturing. Int J Adv Manuf Technol 84(1–4):17–28CrossRefGoogle Scholar
  34. 34.
    Zhou JJ, Yao XF (2016) A hybrid artificial bee colony algorithm for optimal selection of qos-based cloud manufacturing service composition. Int J Adv Manuf Technol 88(9–12):3371–3387Google Scholar
  35. 35.
    Li CS, Wang SL, Kang L, Guo L, Cao Y (2014) Trust evaluation model of cloud manufacturing service platform. Int J Adv Manuf Technol 75(1–4):489–501CrossRefGoogle Scholar
  36. 36.
    Laili YJ, 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):671–690CrossRefGoogle Scholar
  37. 37.
    Liu B, Zhang ZL (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
  38. 38.
    Yang SL, Yang M, Wang S, Huang KD (2016) Adaptive immune genetic algorithm for weapon system portfolio optimization in military big data environment. Cluster Comput 19(3):1359–1372CrossRefGoogle Scholar
  39. 39.
    Zhu B, Xu ZS, Zhang R, Hong M (2016) Hesitant analytic hierarchy process. Eur J Oper Res 250(2):602–614MathSciNetCrossRefzbMATHGoogle Scholar
  40. 40.
    Wu XL, Li RW, Cao YL, Ni YH, Xu X, Qian XY (2016) The value network optimization research based on the analytic hierarchy process method and the dynamic programming of cloud manufacturing. Int J Adv Manuf Technol 84(1–4):1–9Google Scholar
  41. 41.
    Zhao J, Jin JL, Zhu JZ, Xu JC, Hang QF, Chen YQ, Han DH (2016) Water resources risk assessment model based on the subjective and objective combination weighting methods. Water Resour Manag 30(9):3027–3042CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Yi Que
    • 1
  • Wei Zhong
    • 2
  • Hailin Chen
    • 1
  • Xinan Chen
    • 1
  • Xu Ji
    • 1
  1. 1.College of Chemical EngineeringSichuan UniversityChengduPeople’s Republic of China
  2. 2.China Construction West Construction Co LtdChengduPeople’s Republic of China

Personalised recommendations