Optimal Chiller Loading by MOEA/D for Reducing Energy Consumption

  • Yong Wang
  • Jun-qing Li
  • Mei-xian Song
  • Li Li
  • Pei-yong Duan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10954)


A modified multi-objective evolutionary algorithm based on decomposition (MOEA/D) is used to solve the optimal chiller loading (OCL) problem. In a multi-chiller system, the chillers are usually partially loaded for most of the running time. If the chillers are unreasonably managed, their consumption noticeably increases. To reduce power consumption, the partial load ratio (PLR) of each chiller must be adjusted. The system must meet the system cooling load (CL), so, it is a constrained optimization problem. This study uses a multi-objective method to solve the constrained optimization. The constraint condition is changed to a new objective, so, the problem can be solved as a multi-objective problem. Comparison with the experimental results in the literature proved the effectiveness and performance of the modified algorithm, which can be fully applied in air conditioning system operations.


Optimal chiller loading Decomposition strategy Constrained optimization 



This research is partially supported by National Science Foundation of China under Grant 61773192, 61773246 and 61503170, Shandong Province Higher Educational Science and Technology Program (J17KZ005), Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education (K93-9-2017-02), and State Key Laboratory of Synthetical Automation for Process Industries (PAL-N201602).


  1. 1.
    Chang, Y.C., Lin, F.A., Lin, C.H.: Optimal chiller sequencing by branch and bound method for saving energy. Energy Convers. Manage. 46(13–14), 2158–2172 (2005)CrossRefGoogle Scholar
  2. 2.
    Chang, Y.C.: A novel energy conservation method—optimal chiller loading. Electr. Pow. Syst. Res. 69(2), 221–226 (2004)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Chang, Y.C.: Genetic algorithm based optimal chiller loading for energy conservation. Appl. Therm. Eng. 25(17–18), 2800–2815 (2005)CrossRefGoogle Scholar
  4. 4.
    Salari, E., Askarzadeh, A.: A new solution for loading optimization of multi-chiller systems by general algebraic modeling system. Appl. Therm. Eng. 84(4), 429–436 (2015)CrossRefGoogle Scholar
  5. 5.
    Jeyadevi, S., Baskar, S., Babulal, C.K.: Solving multiobjective optimal reactive power dispatch using modified NSGA-II. Int. J. Elec. Power. 33(2), 219–228 (2011)CrossRefGoogle Scholar
  6. 6.
    Deb, K., Pratap, A., Agarwal, S.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evolut. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  7. 7.
    Coello, C.A.C., Pulido, G.T., Lechuga, M.S.: Handling multiple objectives with particle swarm optimization. IEEE Trans. Evolut. Comput. 8(3), 256–279 (2004)CrossRefGoogle Scholar
  8. 8.
    Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)CrossRefGoogle Scholar
  9. 9.
    Li, H., Landa-Silva, D.: An adaptive evolutionary multi-objective approach based on simulated annealing. Evol. Comput. 19(4), 561–595 (2014)CrossRefGoogle Scholar
  10. 10.
    Zhao, F., Chen, Z., Zhang, C.: A modified MOEA/D with adaptive mutation mechanism for multi-objective job shop scheduling problem. J. Comput. Inform. Syst. 11(8), 2833–2840 (2015)Google Scholar
  11. 11.
    Souza, M.Z.D., Pozo, A.T.R.: A GPU implementation of MOEA/D-ACO for the multiobjective traveling salesman problem. IEEE Intell. Syst., 324–329 (2014)Google Scholar
  12. 12.
    Lu, H., Zhu, Z., Wang, X., Yin, L.: A variable neighborhood MOEA/D for multiobjective test task scheduling problem. Math. Probl. Eeg. 2014(3), 1–14 (2014)Google Scholar
  13. 13.
    Konstantinidis, A., Yang, K.: Multi-objective energy-efficient dense deployment in wireless sensor networks using a hybrid problem-specific MOEA/D. Appl. Soft Comput. 11(6), 4117–4134 (2011)CrossRefGoogle Scholar
  14. 14.
    Li, H., Zhang, Q.: Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE Trans. Evol. Comput. 13(2), 284–302 (2009)CrossRefGoogle Scholar
  15. 15.
    Zhang, Q., Liu, W., Li, H.: The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances. In: 2009 IEEE Congress on Evolutionary Computation, pp. 203–208. IEEE Press (2009)Google Scholar
  16. 16.
    Lin, S., Lin, F., Chen, H., Zeng, W.: A MOEA/D-based multi-objective optimization algorithm for remote medical. Neurocomputing 220, 5–16 (2016)CrossRefGoogle Scholar
  17. 17.
    Wang, Z., Zhang, Q., Zhou, A., Gong, M., Jiao, L.: Adaptive replacement strategies for MOEA/D. IEEE Trans. Cybern. 46(2), 474–486 (2017)CrossRefGoogle Scholar
  18. 18.
    Qi, Y., Ma, X., Liu, F., Jiao, L., Sun, J., Wu, J.: MOEA/D with adaptive weight adjustment. Evol. Comput. 22(2), 231–264 (2014)CrossRefGoogle Scholar
  19. 19.
    Lu, H., Zhang, M., Fei, Z., Mao, K.: Multi-objective energy consumption scheduling based on decomposition algorithm with the non-uniform weight vector. Appl. Soft Comput. 39(C), 223–239 (2016)CrossRefGoogle Scholar
  20. 20.
    Zhang, J., Tang, Q., Li, P., Deng, D., Chen, Y.: A modified MOEA/D approach to the solution of multi-objective optimal power flow problem. Appl. Soft Comput. 47(C), 494–514 (2016)CrossRefGoogle Scholar
  21. 21.
    Meng, Z., Shen, R., Jiang, M.: A penalty function algorithm with objective parameters and constraint penalty parameter for multi-objective programming. Am. J. Oper. Res. 4(6), 331–339 (2014)CrossRefGoogle Scholar
  22. 22.
    Chekir, N., Bellagi, A.: Performance improvement of a Butane/Octane absorption chiller. Energy 36(10), 6278–6284 (2011)CrossRefGoogle Scholar
  23. 23.
    Li, J.Q., Sang, H.Y., Han, Y.Y., Wang, C.G., Gao, K.Z.: Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions. J. Clean. Prod. 181, 584–598 (2018)CrossRefGoogle Scholar
  24. 24.
    Zheng, Z.X., Li, J.Q.: Optimal chiller loading by improved invasive weed optimization algorithm for reducing energy consumption. Energy Buildings 161, 80–88 (2018)CrossRefGoogle Scholar
  25. 25.
    Duan, P.Y., Li, J.Q., Wang, Y., Sang, H.Y., Jia, B.X.: Solving chiller loading optimization problems using an improved teaching-learning-based optimization algorithm. Optim. Contr. Appl. Met. 39(1), 65–77 (2018)CrossRefGoogle Scholar
  26. 26.
    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)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Li, J.Q., Pan, Q.K.: Chemical-reaction optimization for solving fuzzy job-shop scheduling problem with flexible maintenance activities. Int. J. Prod. Econ. 145(1), 4–17 (2013)CrossRefGoogle Scholar
  28. 28.
    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, 279–292 (2015)CrossRefGoogle Scholar
  29. 29.
    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. Tech. 55(9–12), 1159–1169 (2011)CrossRefGoogle Scholar
  30. 30.
    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
  31. 31.
    Li, J.Q., Pan, Q.K., Chen, J.: A hybrid pareto-based local search algorithm for multi-objective flexible job shop scheduling problems. Int. J. Prod. Res. 50(4), 1063–1078 (2012)CrossRefGoogle Scholar
  32. 32.
    Li, J.Q., Pan, Q.K., Suganthan, P.N., Chua, T.J.: A hybrid tabu search algorithm with an efficient neighborhood structure for the flexible job shop scheduling problem. Int. J. Adv. Manuf. Tech. 59(4), 647–662 (2011)Google Scholar
  33. 33.
    Li, J.Q., Pan, Y.X.: A hybrid discrete particle swarm optimization algorithm for solving fuzzy job shop scheduling problem. Int. J. Adv. Manuf. Tech. 66(1–4), 583–596 (2013)CrossRefGoogle Scholar
  34. 34.
    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
  35. 35.
    Li, J.Q., Pan, Q.K.: Solving the large-scale hybrid flow shop scheduling problem with limited buffers by a hybrid artificial bee colony algorithm. Inf. Sci. 316(20), 487–502 (2015)CrossRefGoogle Scholar
  36. 36.
    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
  37. 37.
    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
  38. 38.
    Li, J.Q., Pan, Q.K., Mao, K., Suganthan, P.N.: Solving the steelmaking casting problem using an effective fruit fly optimisation algorithm. Knowl.-Based Syst. 72(12), 28–36 (2014)CrossRefGoogle Scholar
  39. 39.
    Li, J.Q., Pan, Q.K.: Chemical-reaction optimization for flexible job-shop scheduling problems with maintenance activity. Appl. Soft Comput. 12(9), 2896–2912 (2012)CrossRefGoogle Scholar
  40. 40.
    Li, J.Q., Pan, Q.K., Wang, F.T.: A hybrid variable neighborhood search for solving the hybrid flow shop scheduling problem. Appl. Soft Comput. 24, 63–77 (2014)CrossRefGoogle Scholar
  41. 41.
    Li, J.Q., Pan, Q.K., Xie, S.X., Wang, S.: A hybrid artificial bee colony algorithm for flexible job shop scheduling problems. Int. J. Comput. Commun. Control 6(2), 267–277 (2011)CrossRefGoogle Scholar
  42. 42.
    Li, J.Q., Pan, Q.K., Xie, S.X.: A hybrid variable neighborhood search algorithm for solving multi-objective flexible job shop problems. ComSIS Comput. Sci. Inf. Syst. 7(4), 907–930 (2010)CrossRefGoogle Scholar
  43. 43.
    Li, J.Q., Wang, J.D., Pan, Q.K., Duan, P.Y., Sang, H.Y., Gao, K.Z., Xue, Y.: A hybrid artificial bee colony for optimizing a reverse logistics network system. Soft Comput. 21(20), 6001–6018 (2017)CrossRefGoogle Scholar
  44. 44.
    Zhang, P., Liu, H., Ding, Y.H.: Dynamic bee colony algorithm based on multi-species co-evolution. Appl. Intell. 40, 427–440 (2014)CrossRefGoogle Scholar
  45. 45.
    Hu, C.Y., Liu, H., Zhang, P.: Cooperative co-evolutionary artificial bee colony algorithm based on hierarchical communication model. Chin. J. Elec. 25, 570–576 (2016)CrossRefGoogle Scholar
  46. 46.
    Liu, Y., Jiao, Y.C., Zhang, Y.M., Tan, Y.Y.: Synthesis of phase-only reconfigurable linear arrays using multiobjective invasive weed optimization based on decomposition. Int. J. Antenn. Propag. 2014 (2014)Google Scholar
  47. 47.
    Zheng, X.W., Lu, D.J., Wang, X.G., Liu, H.: A cooperative coevolutionary biogeography-based optimizer. Appl. Intell. 43, 95–111 (2015)CrossRefGoogle Scholar
  48. 48.
    Liu, H., Zhang, P., Hu, B., Moore, P.: A novel approach to task assignment in a cooperative multi-agent design system. Appl. Intell. 43, 162–175 (2015)CrossRefGoogle Scholar
  49. 49.
    Zhang, Z.J., Liu, H.: Social recommendation model combining trust propagation and sequential behaviors. Appl. Intell. 43, 695–706 (2015)CrossRefGoogle Scholar
  50. 50.
    Wang, J.L., Gong, B., Liu, H., Li, S.H.: Model and algorithm for heterogeneous scheduling integrated with energy-efficiency awareness. T.I. Meas. Control. 38, 452–462 (2016)CrossRefGoogle Scholar
  51. 51.
    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
  52. 52.
    Wang, J.L., Gong, B., Liu, H., Li, S.H., Yi, J.: Heterogeneous computing and grid scheduling with parallel biologically inspired hybrid heuristics. T.I. Meas. Control 36, 805–814 (2014)CrossRefGoogle Scholar
  53. 53.
    Bai, J., Liu, H.: Multi-objective artificial bee algorithm based on decomposition by PBI method. Appl. Intell. 45(4), 976–991 (2016)CrossRefGoogle Scholar
  54. 54.
    Dong, X., Zhang, H., Sun, J., Wan, W.: A two-stage learning approach to face recognition. J. Vis. Commun. Image R. 43, 21–29 (2017)CrossRefGoogle Scholar
  55. 55.
    Jia, W., Zhao, D., Zheng, Y., Hou, S.: A novel optimized GA–Elman neural network algorithm. Neural Comput. Appl. 6, 1–11 (2017)Google Scholar
  56. 56.
    Zheng, X., Yu, X., Yan, L., Liu, H.: An enhanced multi-objective group search optimizer based on multi-producer and crossover operator. J. Inf. Sci. Eng. 37(1), 33–50 (2017)MathSciNetGoogle Scholar
  57. 57.
    Liu, H., Chen, Z.H., Zheng, X.W., Hu, B., Lu, D.J., Chen, Z.H.: Energy-efficient virtual network embedding in networks for cloud computing. Int. J. Web Grid Serv. 13(1–1), 75 (2017)CrossRefGoogle Scholar
  58. 58.
    Xiao, X., Zheng, X., Zhang, Y.: A multidomain survivable virtual network mapping algorithm. Secur. Commun. Netw. 2017(10), 1–12 (2017)CrossRefGoogle Scholar
  59. 59.
    Han, Y.Y., Gong, D.W., Jin, Y.C., Pan, Q.K.: Evolutionary multiobjective blocking lot-streaming flow shop scheduling with machine breakdowns. IEEE T. Cybern. PP(99), 1–14 (2017)Google Scholar
  60. 60.
    Han, Y.Y., Gong, D.W., Sun, X.Y.: An improved NSGA-II algorithm for multi-objective lot-streaming flow shop scheduling problem. Int. J. Prod. Res. 52(8), 2211–2231 (2014)CrossRefGoogle Scholar
  61. 61.
    Gong, D.W., Han, Y.Y., Sun, J.Y.: A novel hybrid multi-objective artificial bee colony algorithm for the blocking lot-streaming flow shop scheduling problems. Knowl.-Based Syst. 148, 115–130 (2018)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of ComputerLiaocheng UniversityLiaochengChina
  2. 2.School of InformationShandong Normal UniversityJinanChina
  3. 3.China Key Laboratory of Computer Network and Information Integration, Ministry of EducationSoutheast UniversityNanjingPeople’s Republic of China
  4. 4.State Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyangChina

Personalised recommendations