Advertisement

A dynamic ant-colony genetic algorithm for cloud service composition optimization

  • Yefeng Yang
  • Bo YangEmail author
  • Shilong Wang
  • Feng Liu
  • Yankai Wang
  • Xiao Shu
ORIGINAL ARTICLE
  • 83 Downloads

Abstract

At present, as the candidate services in the cloud service pool increase, the scale of the service composition increases rapidly. When the existing intelligent optimization algorithms are used to solve the large-scale cloud service composition and optimization (CSCO) problem, it is difficult to ensure the high precision and stability of the optimization results. To overcome such drawbacks, a new dynamic ant-colony genetic hybrid algorithm (DAAGA) is proposed in this paper. The best fusion evaluation strategy is used to determine the invoking timing of genetic and ant-colony algorithms, so the executive time of the two algorithms can be controlled dynamically based on the current solution quality, then the optimization ability is maximized and the overall convergence speed is accelerated. An iterative adjustment threshold is introduced to control the genetic operation and population size in later iterations, in which the effect of genetic algorithm is reduced when the population closes to optimal solution, only the mutation operation is implemented to reduce the calculation, and the population size is increased to find the optimal solution more quickly. A series of comparison experiments are carried out and the results show that the accuracy and stability of DAAGA are significantly improved for the large-scale CSCO problem, and the time consumption of the algorithm is also optimized.

Keywords

Cloud manufacturing Service composition and optimization Quality of service DAAGA Best fusion point assessment Iterative adjustment threshold 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgments

The presented work was supported by the Self-Planned Task of State Key Laboratory of Mechanical Transmission (grant number SKLMT-ZZKT-2016Z04), the Fundamental Research Funds for the Central Universities (grant number 106112017CDJXY110001, 2018CDQYJX0013), and the open research fund project of state key laboratory of complex product intelligent manufacturing system technology (grant number QYYE602).

Supplementary material

170_2018_3215_MOESM1_ESM.xlsx (188 kb)
ESM 1 (XLSX 187 kb)

References

  1. 1.
    Li BH, Zhang L, Wang SL, Tao F, Cao JW, Jiang XD, Song X, Chai XD (2010) Cloud manufacturing: a new service-oriented networked manufacturing model. Comput Integr Manuf Syst 16(1):1–16Google Scholar
  2. 2.
    Zhou J, Yao X (2015) Advanced manufacturing technology and new industrial revolution. Comput Integr Manuf Syst 21(8):1963–1978Google Scholar
  3. 3.
    Arkat J, Ghahve H (2014) Scheduling of virtual manufacturing cells with outsourcing allowed. Int J Comput Integr Manuf 27(12):1079–1089.  https://doi.org/10.1080/0951192x.2013.874581 CrossRefGoogle Scholar
  4. 4.
    Li BH, Zhang L, Ren L, Chai XD, Tao F, Wang YZ, Yin C, Huang P, Zhao XP, Zhou ZD (2012) Typical characteristics, technologies and applications of cloud manufacturing. Comput Integr Manuf Syst 18(7):1345–1356Google Scholar
  5. 5.
    Luo YL, Zhang L, Tao F, Ren L, Liu YK, Zhang ZQ (2013) A modeling and description method of multidimensional information for manufacturing capability in cloud manufacturing system. Int J Adv Manuf Technol 69(5–8):961–975.  https://doi.org/10.1007/s00170-013-5076-9 CrossRefGoogle Scholar
  6. 6.
    Buckholtz B, Ragai I, Wang L (2015) Cloud manufacturing: current trends and future implementations. Journal of manufacturing science and engineering-transactions of the Asme 137(4):044001.  https://doi.org/10.1115/1.4030009 CrossRefGoogle Scholar
  7. 7.
    Zhou J, Yao X (2017) Hybrid teaching-learning-based optimization of correlation-aware service composition in cloud manufacturing. Int J Adv Manuf Technol 91(9–12):3515–3533.  https://doi.org/10.1007/s00170-017-0008-8 CrossRefGoogle Scholar
  8. 8.
    Antonio Parejo J, Segura S, Fernandez P, Ruiz-Cortes A (2014) QoS-aware web services composition using GRASP with path relinking. Expert Syst Appl 41(9):4211–4223.  https://doi.org/10.1016/j.eswa.2013.12.036 CrossRefGoogle Scholar
  9. 9.
    Cao Y, Wang S, Kang L, Gao Y (2016) A TQCS-based service selection and scheduling strategy in cloud manufacturing. Int J Adv Manuf Technol 82(1–4):235–251.  https://doi.org/10.1007/s00170-015-7350-5 CrossRefGoogle Scholar
  10. 10.
    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.  https://doi.org/10.1007/s00170-012-3939-0 CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    Xue X, Wang S, Lu B (2016) Manufacturing service composition method based on networked collaboration mode. J Netw Comput Appl 59:28–38CrossRefGoogle Scholar
  13. 13.
    Wang ZJ, Liu ZZ, Zhou XF, Lou YS (2011) An approach for composite web service selection based on DGQoS. Int J Adv Manuf Technol 56(9–12):1167–1179.  https://doi.org/10.1007/s00170-011-3230-9 CrossRefGoogle Scholar
  14. 14.
    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.  https://doi.org/10.1007/s00170-015-7222-z CrossRefGoogle Scholar
  15. 15.
    Wang SL, Guo L, Kang L, Li CS, Li XY, Stephane YM (2014) Research on selection strategy of machining equipment in cloud manufacturing. Int J Adv Manuf Technol 71(9–12):1549–1563.  https://doi.org/10.1007/s00170-013-5578-5 CrossRefGoogle Scholar
  16. 16.
    Huang B, Li C, 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.  https://doi.org/10.1080/17517575.2013.792396 CrossRefGoogle Scholar
  17. 17.
    Guo H, Tao F, Zhang L, Su S, Si N (2010) Correlation-aware web services composition and QoS computation model in virtual enterprise. Int J Adv Manuf Technol 51(5–8):817–827.  https://doi.org/10.1007/s00170-010-2648-9 CrossRefGoogle Scholar
  18. 18.
    Tao F, Zhao D, Hu Y, Zhou Z (2010) Correlation-aware resource service composition and optimal-selection in manufacturing grid. Eur J Oper Res 201(1):129–143.  https://doi.org/10.1016/j.ejor.2009.02.025 CrossRefzbMATHGoogle Scholar
  19. 19.
    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.  https://doi.org/10.1002/int.21865 CrossRefGoogle Scholar
  20. 20.
    Jin H, Yao X, Chen Y (2017) Correlation-aware QoS modeling and manufacturing cloud service composition. J Intell Manuf 28(8):1947–1960.  https://doi.org/10.1007/s10845-015-1080-2 CrossRefGoogle Scholar
  21. 21.
    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 47(3):721–742.  https://doi.org/10.1007/s10489-017-0927-y CrossRefGoogle Scholar
  22. 22.
    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):3371–3387.  https://doi.org/10.1007/s00170-016-9034-1 CrossRefGoogle Scholar
  23. 23.
    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):4765–4784.  https://doi.org/10.1080/00207543.2017.1292064 CrossRefGoogle Scholar
  24. 24.
    He W, Xu L (2015) A state-of-the-art survey of cloud manufacturing. Int J Comput Integr Manuf 28(3):239–250CrossRefGoogle Scholar
  25. 25.
    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.  https://doi.org/10.1108/IJWIS-12-2015-0040 CrossRefGoogle Scholar
  26. 26.
    Wu QW, Ishikawa F, Zhu QS, Shin DH (2016) QoS-aware multigranularity service composition: modeling and optimization. IEEE Trans Syst Man CY-S 46(11):1565–1577.  https://doi.org/10.1109/TSMC.2015.2503384 CrossRefGoogle Scholar
  27. 27.
    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.  https://doi.org/10.1109/tse.2004.11 CrossRefGoogle Scholar
  28. 28.
    Nabaei A, Hamian M, Parsaei MR, Safdari R, Samad-Soltani T, Zarrabi H, Ghassemi A (2018) Topologies and performance of intelligent algorithms: a comprehensive review. Artif Intell Rev 49(1):79–103.  https://doi.org/10.1007/s10462-016-9517-3 CrossRefGoogle Scholar
  29. 29.
    Al-Shihabi ST, AlDurgam MM (2017) A max–min ant system for the finance-based scheduling problem. Comput Ind Eng 110:264–276.  https://doi.org/10.1016/j.cie.2017.06.016 CrossRefGoogle Scholar
  30. 30.
    Li X, Wang Y (2018) Scheduling batch processing machine using max–min ant system algorithm improved by a local search method. Math Probl Eng 2018:1–10.  https://doi.org/10.1155/2018/3124182 MathSciNetGoogle Scholar
  31. 31.
    Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344(2–3):243–278.  https://doi.org/10.1016/j.tcs.2005.05.020 MathSciNetCrossRefzbMATHGoogle Scholar
  32. 32.
    Li X, Mao Z, Qi E, Acm (2009) Research on multi-supplier performance measurement based on genetic ant colony algorithm. World summit on genetic and evolutionary computation 2009:867–870  https://doi.org/10.1145/1543834.1543962
  33. 33.
    Dong G, Guo WW, Tickle K (2012) Solving the traveling salesman problem using cooperative genetic ant systems. Expert Syst Appl 39(5):5006–5011.  https://doi.org/10.1016/j.eswa.2011.10.012 CrossRefGoogle Scholar
  34. 34.
    Zhao F, Yao Z, Luan J, Song X (2016) A novel fused optimization algorithm of genetic algorithm and ant colony optimization. Math Probl Eng 2016:1–10.  https://doi.org/10.1155/2016/2167413 zbMATHGoogle Scholar
  35. 35.
    Yao Z, Liu J, Wu Z (2009) An integrated optimization algorithm of GA and ACA-based approaches for modeling virtual enterprise partner selection. Data Base Adv Inf Syst 40(2):37–56.  https://doi.org/10.1145/1531817.1531824 CrossRefGoogle Scholar
  36. 36.
    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.  https://doi.org/10.1080/0951192X.2018.1529435 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
  • Feng Liu
    • 2
  • Yankai Wang
    • 1
  • Xiao Shu
    • 1
  1. 1.State Key Laboratory of Mechanical TransmissionChongqing UniversityChongqingChina
  2. 2.State Key Laboratory of Complex Product Intelligent Manufacturing System Technology, CASICBeijingChina

Personalised recommendations