Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

DE-caABC: differential evolution enhanced context-aware artificial bee colony algorithm for service composition and optimal selection in cloud manufacturing

  • 320 Accesses

  • 20 Citations

Abstract

Cloud manufacturing (CMfg) is a new type of service-oriented manufacturing paradigm, in which all kinds of manufacturing resources are encapsulated as manufacturing services (MSs) and can be invoked by customers on demand. Manufacturing service composition (MSC) is a key technology in CMfg for creating value-added services to complete complicated manufacturing tasks by aggregating qualified MSs together. However, current MSC approaches have some drawbacks and there still exist some issues remained to be solved: (1) large quantities of candidate services increase the complexity of service dynamic composition, which poses scalability concerns and on-demand efficient solutions; (2) the service domain features (e.g., service prior, correlation, and similarity) that have a strong influence on the efficiency of service composition are not considered adequately, which causes undesirable efficiency in practical service applications; and (3) dynamic characteristics of QoS (quality of service) values in an open network environment are not considered adequately. To effectively address such problems, this paper first proposes a context-aware artificial bee colony (caABC) algorithm based on the principle of ABC and service features in the cloud environment. Then the differential evolution-enhanced caABC, i.e., the so-called DE-caABC, is designed to increase the searching performance of ABC further. Additionally, dynamics of trust QoS is investigated with the introduction of time decay function. Finally, the feasibility and effectiveness of DE-caABC are validated through the experiments.

This is a preview of subscription content, log in to check access.

References

  1. 1.

    Ardagna D, Pernici B (2005) Global and local QoS guarantee in Web service selection. Paper presented at the 2005 International Business Process Management Workshops Berlin,

  2. 2.

    Ardagna D, Pernici B (2007) Adaptive service composition in flexible processes. IEEE Trans Softw Eng 33(6):369–384

  3. 3.

    Bäck T (1994) Selective pressure in evolutionary algorithms: a characterization of selection mechanisms. In: IEEE Int Conf Comput Intell, IEEE, pp 57–62

  4. 4.

    Bravo M (2014) Similarity measures for web service composition models. Int J Web Serv Comput 5(1):1–16

  5. 5.

    Chakaravarthy GV, Marimuthu S, Sait AN (2013) Performance evaluation of proposed differential evolution and particle swarm optimization algorithms for scheduling m-machine flow shops with lot streaming. J Intell Manuf 24(1):175–191

  6. 6.

    Gao ZP, Jian C, Qiu XS, Meng LM (2009) QoE/QoS driven simulated annealing-based genetic algorithm for Web services selection. J China Univ Posts Telecommunications 16:102–107

  7. 7.

    Guo H, Tao F, Zhang L, Su SY, 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

  8. 8.

    Guo H, Tao F, Zhang L, Laili YJ, Liu DK (2012) Research on measurement method of resource service composition flexibility in service-oriented manufacturing system. Int J Comput Integr Manuf 25(2):113–135

  9. 9.

    Helo P, Suorsa M, Hao Y, Anussornnitisarn P (2014) Toward a cloud-based manufacturing execution system for distributed manufacturing. Comput Ind 65(4):646–656

  10. 10.

    Li Hf, Jiang R, Ge Sy (2014) Researches on manufacturing cloud service composition & optimization approach supporting for service statistic correlation. In: 26th Chinese Control and Decision Conference, pp 4149–4154

  11. 11.

    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

  12. 12.

    Islam SM, Das S, Ghosh S, Roy S, Suganthan PN (2012) An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization. IEEE Trans Syst Man Cybern Part B-Cybern 42(2):482–500

  13. 13.

    Kang GS, Tang MD, Liu JX, Liu F, Cao BQ Diversifying web service recommendation results via exploring service usage history. IEEE Trans Serv Comput. doi:10.1109/TSC.2015.2415807

  14. 14.

    Kao YC, Chen CC (2013) A differential evolution fuzzy clustering approach to machine cell formation. Int J Adv Manuf Technol 65(9–12):1247–1259

  15. 15.

    Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department

  16. 16.

    Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471

  17. 17.

    Karen I, Kaya N, Ozturk F (2015) Intelligent die design optimization using enhanced differential evolution and response surface methodology. J Intell Manuf 26(5):1027–1038

  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

  19. 19.

    Laili Y, Tao F, Zhang L, Cheng Y, Luo Y, Sarker BR (2013) A ranking chaos algorithm for dual scheduling of cloud service and computing resource in private cloud. Comput Ind 64(4):448–463

  20. 20.

    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):4380–4404

  21. 21.

    Li XT, Fan YS (2009) Analyzing compatibility and similarity of Web service processes. Chin J Comput 32(12):2429–2437

  22. 22.

    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):1–16

  23. 23.

    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):489–501

  24. 24.

    Li JR, Tao F, Cheng Y, Zhao LJ (2015) Big data in product lifecycle management. Int J Adv Manuf Technol 81(1–4):667–684

  25. 25.

    Mallipeddi R, Suganthan PN, Pan QK, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11(2):1679–1696

  26. 26.

    Ngoko Y, Goldman A, Milojicic D (2013) Service selection in web service compositions optimizing energy consumption and service response time. J Int Serv and Appl 4(1):1–12

  27. 27.

    Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417

  28. 28.

    Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

  29. 29.

    Tao F, Zhao DM, Hu YF, Zhou ZD (2008) Resource service composition and its optimal-selection based on particle swarm optimization in manufacturing grid system. IEEE Trans Ind Inf 4(4):315–327

  30. 30.

    Tao F, Hu YF, Zhao DM, Zhou ZD, Zhang HJ, Lei ZZ (2009a) Study on manufacturing grid resource service QoS modeling and evaluation. Int J Adv Manuf Technol 41(9–10):1034–1042

  31. 31.

    Tao F, Hu YF, Zhou ZD (2009b) Application and modeling of resource service trust-QoS evaluation in manufacturing grid system. Int J Prod Res 47(6):1521–1550

  32. 32.

    Tao F, Hu Y, Zhao D, Zhou Z (2009c) An approach to manufacturing grid resource service scheduling based on trust-QoS. Int J Comput Integr Manuf 22(2):100–111

  33. 33.

    Tao F, Zhao DM, Hu YF, Zhou ZD (2010a) Correlation-aware resource service composition and optimal-selection in manufacturing grid. Eur J Oper Res 201(1):129–143

  34. 34.

    Tao F, Zhao D, Zhang L (2010b) Resource service optimal-selection based on intuitionistic fuzzy set and non-functionality QoS in manufacturing grid system. Knowl and Inf Syst 25(1):185–208

  35. 35.

    Tao F, Zhang L, Venkatesh VC, Luo Y, Cheng Y (2011) Cloud manufacturing: a computing and service-oriented manufacturing model. Pro Inst Mech Eng Part B-J Eng Manuf 225(B10):1969–1976

  36. 36.

    Tao F, LaiLi Y, Xu L, Zhang L (2013) Optimal-selection in cloud manufacturing system. IEEE Trans Ind Inf 9(4):2023–2033

  37. 37.

    Tao F, Zuo Y, Xu LD, Zhang L (2014a) IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing. IEEE Trans Ind Inf 10(2):1547–1557

  38. 38.

    Tao F, Cheng Y, Xu LD, Zhang L, Li BH (2014b) CCIoT-CMfg: cloud computing and internet of things-based cloud manufacturing service system. IEEE Trans Ind Inf 10(2):1435–1442

  39. 39.

    Tao F, Zhang L, Liu Y, Cheng Y, Wang L, Xu X (2015) Manufacturing service management in cloud manufacturing: overview and future research directions. J Manuf Sci Eng-Trans ASME 137(4)

  40. 40.

    Valilai OF, Houshmand M (2014) A platform for optimisation in distributed manufacturing enterprises based on cloud manufacturing paradigm. Int J Comput Integr Manuf 27(11):1031–1054

  41. 41.

    Wang YW (2009) Application of chaos ant colony algorithm in web service composition based on QoS. Paper presented at the 2009 International Forum on Information Technology and Applications, Vol 2, Proceedings,

  42. 42.

    Wang HY, Li SR (2014) Service substitution method based on composition context. J. Communications 35(9):57–66

  43. 43.

    Wang S, Sun Q, Yang F (2010) Towards web service selection based on QoS estimation. Int J Web and Grid Services 6(4):424–443

  44. 44.

    Wang ZJ, Liu ZZ, Zhou XF, Lou YS (2011a) An approach for composite web service selection based on DGQoS. Int J Adv Manuf Technol 56(9–12):1167–1179

  45. 45.

    Wang Y, Cai ZX, Zhang QF (2011b) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evol Comput 15(1):55–66

  46. 46.

    Wang Y, Cai ZX, Zhang QF (2012) Enhancing the search ability of differential evolution through orthogonal crossover. Inf Sci 185(1):153–177

  47. 47.

    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

  48. 48.

    Wang DD, Yang Y, Mi ZQ (2015) A genetic-based approach to web service composition in geo-distributed cloud environment. Comput Electr Eng 43:129–141

  49. 49.

    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

  50. 50.

    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. Central Eur J Oper Res 22(4):663–685

  51. 51.

    Xu X (2012) From cloud computing to cloud manufacturing. Robot Comput Integr Manuf 28(1):75–86

  52. 52.

    Xue X, Liu ZZ, Wang SF (2016) Manufacturing service composition for the mass customised production. Int J Comput Integr Manuf 29(2):119–135

  53. 53.

    Ye S, Wei J, Li L, Huang T (2008) Service-correlation aware service selection for composite service. Chin J Comput 31(8):1383–1397

  54. 54.

    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

  55. 55.

    Zhang MW, Wei WJ, Zhang B, Zhang XZ, Zhu ZL (2008) Research on service selection approach based on composite service execution information. Chin J Comput 31(8):1398–1411

  56. 56.

    Zhang M, Zhang B, Na J, Zhang X, Zhu Z (2009) Composite service selection based on dot pattern mining. Paper presented at the 2009 I.E. Int. Conf. Congress on Services, Los Angeles,

  57. 57.

    Zhang L, Guo H, Tao F, Luo YL, Si N (2010) Flexible management of resource service composition in cloud manufacturing. Paper presented at the 2010 I.E. Int. Conf. Industrial Engineering & Engineering Management,

  58. 58.

    Zhang MW, Zhang B, Zhang XZ, Zhu ZL (2012) A division based composite service selection approach. Comput Res Dev 49(5):1005–1017

  59. 59.

    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

  60. 60.

    Zhang L, Rao K, Wang R (2015) T-QoS-aware based parallel ant colony algorithm for services composition. J Syst Eng Electr 26(5):1100–1106

  61. 61.

    Zhao XC, Song BQ, Huang PY, Wen ZC, Weng JL, Fan Y (2012) An improved discrete immune optimization algorithm based on PSO for QoS-driven web service composition. Appl Soft Comput 12(8):2208–2216

  62. 62.

    Zhou J, Yao X (2016) A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition. Int J Adv Manuf Technol. doi:10.1007/s00170-016-9034-1

Download references

Author information

Correspondence to Xifan Yao.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhou, J., Yao, X. 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, 1085–1103 (2017). https://doi.org/10.1007/s00170-016-9455-x

Download citation

Keywords

  • Cloud manufacturing
  • Manufacturing service composition
  • Service domain feature
  • Quality of service
  • Differential evolution
  • Context awareness
  • Artificial bee colony algorithm