A hybrid multi-objective artificial bee colony algorithm for flexible task scheduling problems in cloud computing system

  • Jun-qing LiEmail author
  • Yun-qi Han


In this study, the flexible task scheduling problem in a cloud computing system is studied and solved by a hybrid discrete artificial bee colony (ABC) algorithm, where the considered problem is firstly modeled as a hybrid flowshop scheduling (HFS) problem. Both a single objective and multiple objectives are considered. In multiple objective HFS problems, three objectives, i.e., minimization of the maximum completion time, maximum device workload, and total workloads of all devices, are considered simultaneously. Two different kinds of HFS are considered, i.e., HFS with identical parallel machines and HFS with unrelated machines. In the proposed algorithm, three types of artificial bees are included as in the classical ABC algorithm, i.e., the employed bee, the onlooker bee, and the scout bee. Each solution is represented as an integer string. To consider the problem features, several different types of perturbation structures are investigated to enhance the searching abilities. An improved version of the adaptive perturbation structure is embedded in the proposed algorithm to balance the exploitation and exploration ability. A simple but efficient selection and updated approach are applied to enhance the exploitation process. To further improve the exploitation abilities, a deep-exploitation operator is designed. An improved scout bee employed with different local search methods for the best food source or the abandoned solution is designed and can increase the convergence ability of the proposed algorithm. The proposed algorithm is tested on sets of the well-known benchmark instances, and the performance of the proposed algorithm is verified.


Hybrid flowshop scheduling problem Artificial bee colony algorithm Cloud system Flexible task scheduling 



This research is partially supported by National Science Foundation of China under Grants 61773192, 61773246 and 61803192, Shandong Province Higher Educational Science and Technology Program (J17KZ005), Special fund plan for local science and technology development lead by central authority, major basic research projects in Shandong (ZR2018ZB0419), and also under Grant of Key Laboratory of Intelligent Optimization and Control with Big Data.


  1. 1.
    Zhang, P., Zhou, M.: Dynamic cloud task scheduling based on a two-stage strategy. IEEE Trans. Autom. Sci. Eng. 15, 772–783 (2017)CrossRefGoogle Scholar
  2. 2.
    Yuan, H., Bi, J., Tan, W., Li, B.H.: Temporal task scheduling with constrained service delay for profit maximization in hybrid clouds. IEEE Trans. Autom. Sci. Eng. 14(1), 337–348 (2017)CrossRefGoogle Scholar
  3. 3.
    Xiong, Y., Huang, S., Wu, M., She, J., Jiang, K.: A Johnson’s-rule-based genetic algorithm for two-stage-task scheduling problem in data-centers of cloud computing. IEEE Trans. Cloud Comput. (2017). CrossRefGoogle Scholar
  4. 4.
    Li, X., Jiang, T., Ruiz, R.: Heuristics for periodical batch job scheduling in a MapReduce computing framework. Inf. Sci. 326, 119–133 (2016)zbMATHCrossRefGoogle Scholar
  5. 5.
    Wang, J.L., Gong, B., Liu, H., Li, S.H.: Multidisciplinary approaches to artificial swarm intelligence for heterogeneous computing and cloud scheduling. Appl. Intell. 43, 662–675 (2015)CrossRefGoogle Scholar
  6. 6.
    Pan, Q.K., Gao, L., Li, X.Y., Framinan, M.: Effective constructive heuristics and meta-heuristics for the distributed assembly permutation flowshop scheduling problem. Appl. Soft Comput. 81, 105492 (2019). CrossRefGoogle Scholar
  7. 7.
    Li, J.Q., Pan, Q.K., Mao, K.: A hybrid fruit fly optimization algorithm for the realistic hybrid flowshop rescheduling problem in steelmaking systems. IEEE Trans. Autom. Sci. Eng. 13(2), 932–949 (2016)CrossRefGoogle Scholar
  8. 8.
    Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Li, J.Q., Pan, Q.K., Duan, P.Y.: An improved artificial bee colony algorithm for solving hybrid flexible flowshop with dynamic operation skipping. IEEE Trans. Cybern. 46(6), 1311–1324 (2016)CrossRefGoogle Scholar
  10. 10.
    Li, J.Q., Bai, S.C., Duan, P.Y., Sang, H.Y., Han, Y.Y., Zheng, Z.X.: An improved artificial bee colony algorithm for addressing distributed flow shop with distance coefficient in a prefabricated system. Int. J. Prod. Res. (2019). CrossRefGoogle Scholar
  11. 11.
    Duan, P.Y., Li, J.Q., Wang, Y., Sang, H., Jia, B.: Solving chiller loading optimization problems using an improved teaching-learning-based optimization algorithm. Optim. Control Appl. Methods. 39(1), 65–77 (2018)zbMATHCrossRefGoogle Scholar
  12. 12.
    Li, J.Q., Pan, Q.K., Mao, K.: A discrete teaching-learning-based optimisation algorithm for realistic flowshop rescheduling problems. Eng. Appl. Artif. Intell. 37(1), 279–292 (2015)CrossRefGoogle Scholar
  13. 13.
    Zheng, Z., Li, J.Q.: Optimal chiller loading by improved invasive weed optimization algorithm for reducing energy consumption. Energy Build. 161, 80–88 (2018)CrossRefGoogle Scholar
  14. 14.
    Sang, H.Y., Pan, Q.K., Duan, P.Y., Li, J.Q.: An effective discrete invasive weed optimization algorithm for lot-streaming flowshop scheduling problems. J. Intell. Manuf. 29(6), 1337–1349 (2018)CrossRefGoogle Scholar
  15. 15.
    Ruiz, R., Pan, Q.K., Naderi, B.: Iterated Greedy methods for the distributed permutation flowshop scheduling problem. Omega 83, 213–222 (2019)CrossRefGoogle Scholar
  16. 16.
    Li, J.Q., Pan, Q.K., Xie, S.X.: An effective shuffled frog-leaping algorithm for multi-objective flexible job shop scheduling problems. Appl. Math. Comput. 218(18), 9353–9371 (2012)MathSciNetzbMATHGoogle Scholar
  17. 17.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  18. 18.
    Li, J.Q., Pan, Q.K., Gao, K.Z.: Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems. Int. J. Adv. Manuf. Technol. 55, 1159–1169 (2011)CrossRefGoogle Scholar
  19. 19.
    Li, J.Q., Pan, Q.K., Tasgetiren, M.F.: A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities. Appl. Math. Model. 38(3), 1111–1132 (2014)MathSciNetzbMATHCrossRefGoogle Scholar
  20. 20.
    Li, J.Q., Tao, X.R., Jia, B.X., Han Y.Y., Liu, C., Duan, P., Zheng, Z.X., Sang, H.Y.: Efficient multi-objective algorithm for the lot-streaming hybrid flowshop with variable sub-lots. Swarm. Evol. Comput. (2019). Scholar
  21. 21.
    Yu, K., While, L., Reynolds, M., Wang, X., Liang, J.J., Zhao, L., Wang, Z.: Multiobjective optimization of ethylene cracking furnace system using self-adaptive multiobjective teaching-learning-based optimization. Energy 148, 469–481 (2018)CrossRefGoogle Scholar
  22. 22.
    Li, S., Wang, N., Jia, T., He, Z., Liang, H.: Multiobjective optimization for multiperiod reverse logistics network design. IEEE Trans. Eng. Manag. 63(2), 223–236 (2016)CrossRefGoogle Scholar
  23. 23.
    Yi, J., Huang, D., Fu, S., He, H., Li, T.: Multi-objective bacterial foraging optimization algorithm based on parallel cell entropy for aluminum electrolysis production process. IEEE Trans. Ind. Electron. 63(4), 1 (2015)CrossRefGoogle Scholar
  24. 24.
    Nita, M.C., Pop, F., Voicu, C., Dobre, C., Xhafa, F.: MOMTH: multi-objective scheduling algorithm of many tasks in Hadoop. Clust. Comput. 18(3), 1011–1024 (2015)CrossRefGoogle Scholar
  25. 25.
    Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)CrossRefGoogle Scholar
  26. 26.
    Yuan, Y., Xu, H., Wang, B., et al.: Balancing convergence and diversity in decomposition-based many-objective optimizers. IEEE Trans. Evol. Comput. 20, 1 (2015)Google Scholar
  27. 27.
    Wang, L., Zhang, Q., Zhou, A., Gong, M., Jiao, L.: Constrained subproblems in a decomposition-based multiobjective evolutionary algorithm. IEEE Trans. Evol. Comput. 20(3), 475–480 (2016)CrossRefGoogle Scholar
  28. 28.
    Wang, R., Zhang, Q., Zhang, T.: Decomposition-based algorithms using pareto adaptive scalarizing methods. IEEE Trans. Evol. Comput. 20(6), 821–837 (2016)CrossRefGoogle Scholar
  29. 29.
    Li, J.Q., Pan, Q.K., Liang, Y.C.: An effective hybrid tabu search algorithm for multi-objective flexible job shop scheduling problems. Comput. Ind. Eng. 59(4), 647–662 (2010)CrossRefGoogle Scholar
  30. 30.
    Han, Y.Y., Gong, D.W., Jin, Y.C., Pan, Q.K.: Evolutionary multi-objective blocking lot-streaming flow shop scheduling with machine breakdowns. IEEE Trans. Cybern. 49, 184–197 (2018)CrossRefGoogle Scholar
  31. 31.
    Ruiz, R., Vázquez-Rodríguez, J.A.: The hybrid flow shop scheduling problem. Eur. J. Oper. Res. 205(1), 1–18 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  32. 32.
    Zhang, B., Pan, Q.K., Gao, L., Zhang, X.L., Sang, H.Y., Li, J.Q.: An effective modified migrating birds optimization for hybrid flowshop scheduling problem with lot streaming. Appl. Soft. Comput. 52, 14–27 (2017)CrossRefGoogle Scholar
  33. 33.
    Li, J.Q., Song, M.X., Wang, L., Duan, P.Y., Han, Y.Y., Sang, H.Y., Pan, Q.K.: Hybrid artificial bee colony algorithm for a parallel batching distributed flow shop problem with deteriorating jobs. IEEE Trans. Cybern. (2019). CrossRefGoogle Scholar
  34. 34.
    Chamnanlor, C., Sethanan, K., Gen, M., Chien, C.F.: Embedding ant system in genetic algorithm for re-entrant hybrid flow shop scheduling problems with time window constraints. J. Intell. Manuf. 28(8), 1915–1931 (2017)CrossRefGoogle Scholar
  35. 35.
    Lei, D., Zheng, Y.: Hybrid flow shop scheduling with assembly operations and key objectives: a novel neighborhood search. Appl. Soft Comput. 61, 122–128 (2017)CrossRefGoogle Scholar
  36. 36.
    Pan, Q.K., Gao, L., Wang, L.: A multi-objective hot-rolling scheduling problem in the compact strip production. Appl. Math. Model. 73, 327–334 (2019)MathSciNetCrossRefGoogle Scholar
  37. 37.
    Li, J.Q., Wang, J.D., Pan, Q.K., Duan, P.Y.: A hybrid artificial bee colony for optimizing a reverse logistics network system. Soft Comput. 21(20), 6001–6018 (2017)CrossRefGoogle Scholar
  38. 38.
    Liao, C.J., Tjandradjaja, E., Chung, T.P.: An approach using particle swarm optimization and bottleneck heuristic to solve hybrid flow shop scheduling problem. Appl. Soft Comput. 12(6), 1755–1764 (2012)CrossRefGoogle Scholar
  39. 39.
    Wang, S.Y., Wang, L., Xu, Y., Zhou, G.: An estimation of distribution algorithm for solving hybrid flow-shop scheduling problem. Acta Autom. Sin. 38(3), 437–443 (2012)MathSciNetzbMATHCrossRefGoogle Scholar
  40. 40.
    Engin, O., Doyen, A.: A new approach to solve hybrid flow shop scheduling problems by artificial immune system. Future Gen. Comput. Syst. 20(6), 1083–1095 (2004)CrossRefGoogle Scholar
  41. 41.
    Alaykyran, K., Engin, O., Doyen, A.: Using ant colony optimization to solve hybrid flow shop scheduling problems. Int. J. Adv. Manuf. Technol. 35(5–6), 541–550 (2007)CrossRefGoogle Scholar
  42. 42.
    Neron, E., Baptiste, P., Gupta, J.N.D.: Solving hybrid flow shop problem using energetic reasoning and global operations. Omega Int. J. Manag. Sci. 29(6), 501–511 (2001)CrossRefGoogle Scholar
  43. 43.
    Pan, Q.K., Tasgetiren, M.F., Suganthan, P.N., Chua, T.J.: A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Inf. Sci. 181(12), 2455–2468 (2010)MathSciNetCrossRefGoogle Scholar
  44. 44.
    Deng, G.L., Xu, Z.H., Gu, X.S.: A discrete artificial bee colony algorithm for minimizing the total flow time in the blocking flow shop scheduling. Chin. J. Chem. Eng. 20(6), 1067–1073 (2012)CrossRefGoogle Scholar
  45. 45.
    Han, Y.Y., Liang, J.J., Pan, Q.K., Li, J.Q., Sang, H.Y., Cao, N.N.: Effective hybrid discrete artificial bee colony algorithms for the total flowtime minimization in the blocking flowshop problem. Int. J. Adv. Manuf. Technol. 67(1–4), 397–414 (2013)CrossRefGoogle Scholar
  46. 46.
    Carlier, J., Neron, E.: An exact method for solving the multi-processor flowshop. Rairo-Oper. Res. 34(1), 1–25 (2000)MathSciNetzbMATHCrossRefGoogle Scholar
  47. 47.
    Liu F., Zhang X.P., Zou F.X., Zeng L.L.: Immune clonal selection algorithm for hybrid flow-shop scheduling problem. In: Proceedings of the Chinese Control and Decision Conference, Guilin, China, pp. 2605–2609. IEEE (2009)Google Scholar
  48. 48.
    Xu Y., Wang L., Zhou G., Wang S.Y.: An effective shuffled frog leaping algorithm for solving hybrid flow-shop scheduling problem. In: Proceedings of the International Conference on Intelligent Computing, Zhengzhou, China, pp. 560–567. Springer (2011)Google Scholar
  49. 49.
    Li, C.D., Yi, J., Wang, H., Zhang, G., Li, J.Q.: Interval data driven construction of shadowed sets with application to linguistic word modelling. Inf. Sci. (2018). CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer ScienceLiaocheng UniversityLiaochengPeople’s Republic of China
  2. 2.School of Information Science and EngineeringShandong Normal UniversityJinanPeople’s Republic of China

Personalised recommendations