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A Novel Flower Pollination Algorithm for Modeling the Boiler Thermal Efficiency

  • Peifeng Niu
  • Jinbai Li
  • Lingfang Chang
  • Xianchen Zhang
  • Rongyan Wang
  • Guoqiang Li
Article
  • 46 Downloads

Abstract

The flower pollination algorithm (FPA) is a nature-inspired optimization algorithm. To improve the solution quality and convergence speed of FPA, we proposed a novel flower pollination algorithm (NFPA) which is a hybrid algorithm based on original FPA and wind driven optimization algorithm. Simulation experiments demonstrate that NFPA has better search performance on classical numerical function optimizations compared with other the state-of-the-art optimization methods. In addition, the NFPA is adopted to optimize parameters of fast learning network to build thermal efficiency model of a 330 MW coal-fired boiler and a well-generalized model is obtained. Experimental results show that the tuned fast learning network model by NFPA has better prediction accuracy and generalization ability than other combination models.

Keywords

Flower pollination algorithm Fast learning network Thermal efficiency Coal-fired boiler 

Notes

Acknowledgements

Project supported by the National Natural Science Foundation of China (Grant Nos. 61573306 and 61403331), Natural Science Foundation of Hebei Province (Grant No. F2016203427). We would like to thank reviewers and editors for their constructive suggestions.

Compliance with Ethical Standards

Conflict of interest

All authors declares that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. 1.
    Tunckaya Y, Koklukaya E (2015) Comparative prediction analysis of 600 MWe coal-fired power plant production rate using statistical and neural-based models. J Energy Inst 88(1):11–18CrossRefGoogle Scholar
  2. 2.
    Li G, Niu P, Wang H, Liu Y (2014) Least Square Fast Learning Network for modeling the combustion efficiency of a 300 WM coal-fired boiler. Neural Netw 51:57–66CrossRefzbMATHGoogle Scholar
  3. 3.
    Tunckaya Y, Koklukaya E (2015) Comparative analysis and prediction study for effluent gas emissions in a coal-fired thermal power plant using artificial intelligence and statistical tools. J Energy Inst 88(2):118–125CrossRefGoogle Scholar
  4. 4.
    Li X, Niu P, Li G, Liu J (2017) An adaptive extreme learning machine for modeling NOx emission of a 300 MW circulating fluidized bed boiler. Neural Process Lett 3:1–20Google Scholar
  5. 5.
    Liu B, Hu J, Yan F, Turkson RF, Lin F (2016) A novel optimal support vector machine ensemble model for NOx emissions prediction of a diesel engine. Measurement 92:183–192CrossRefGoogle Scholar
  6. 6.
    Suntivarakorn R, Treedet W (2016) Improvement of boiler’s efficiency using heat recovery and automatic combustion control system. Energy Proc 100:193–197CrossRefGoogle Scholar
  7. 7.
    Niu P, Ma Y, Li M, Yan S, Li G (2016) A kind of parameters self-adjusting extreme learning machine. Neural Process Lett 44(3):813–830CrossRefGoogle Scholar
  8. 8.
    Li G, Niu P, Zhang W, Liu Y (2013) Model NOx emissions by least squares support vector machine with tuning based on ameliorated teaching–learning-based optimization. Chemom Intell Lab Syst 126:11–20CrossRefGoogle Scholar
  9. 9.
    Li G, Niu P, Duan X, Zhang X (2014) Fast learning network: a novel artificial neural network with a fast learning speed. Neural Comput Appl 24(7–8):1683–1695CrossRefGoogle Scholar
  10. 10.
    Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501CrossRefGoogle Scholar
  11. 11.
    Niu P, Chen K, Ma Y et al (2017) Model turbine heat rate by fast learning network with tuning based on ameliorated krill herd algorithm. Knowl-Based Syst 118:80–92CrossRefGoogle Scholar
  12. 12.
    Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science. MHS’95. IEEE, pp 39–43Google Scholar
  13. 13.
    Kennedy J (2011) Particle swarm optimization. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, Boston, pp 760–766Google Scholar
  14. 14.
    Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Yu WJ, Shen M, Chen WN et al (2014) Differential evolution with two-level parameter adaptation. IEEE Trans Cybern 44(7):1080–1099CrossRefGoogle Scholar
  16. 16.
    Bayraktar Z, Komurcu M, Bossard JA, Werner DH (2013) The wind driven optimization technique and its application in electromagnetics. IEEE Trans Antennas Propag 61(5):2745–2757MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Yang XS (2012) Flower pollination algorithm for global optimization. In: UCNC. pp 240–249Google Scholar
  18. 18.
    Yang XS, Karamanoglu M, He X (2014) Flower pollination algorithm: a novel approach for multiobjective optimization. Eng Optim 46(9):1222–1237MathSciNetCrossRefGoogle Scholar
  19. 19.
    Zhou Y, Wang R (2016) An improved flower pollination algorithm for optimal unmanned undersea vehicle path planning problem. Int J Pattern Recognit Artif Intell 30(04):1659010CrossRefGoogle Scholar
  20. 20.
    Sayed SAF, Nabil E, Badr A (2016) A binary clonal flower pollination algorithm for feature selection. Pattern Recognit Lett 77:21–27CrossRefGoogle Scholar
  21. 21.
    Wang R, Zhou Y, Qiao S, Huang K (2016) Flower pollination algorithm with bee pollinator for cluster analysis. Inf Proc Lett 116(1):1–14CrossRefGoogle Scholar
  22. 22.
    Xu S, Wang Y (2017) Parameter estimation of photovoltaic modules using a hybrid flower pollination algorithm. Energy Convers Manag 144:53–68CrossRefGoogle Scholar
  23. 23.
    Ludwig SA (2012) Clonal selection based genetic algorithm for workflow service selection. In: IEEE congress on evolutionary computation (CEC) 2012. IEEE, pp 1–7Google Scholar
  24. 24.
    Sarangi SK, Panda R, Priyadarshini S, Sarangi A (2016) A new modified firefly algorithm for function optimization. In: International conference on electrical, electronics, and optimization techniques (ICEEOT). IEEE, pp 2944–2949Google Scholar
  25. 25.
    Wong ML, Guo YY (2008) Learning Bayesian networks from incomplete databases using a novel evolutionary algorithm. Decis Support Syst 45(2):368–383CrossRefGoogle Scholar
  26. 26.
    Gao WF, Huang LL, Wang J, Liu SY, Qin CD (2016) Enhanced artificial bee colony algorithm through differential evolution. Appl Soft Comput 48:137–150CrossRefGoogle Scholar
  27. 27.
    Liang HT, Kang FH (2016) Adaptive mutation particle swarm algorithm with dynamic nonlinear changed inertia weight. Optik-Int J Light Electron Opt 127(19):8036–8042CrossRefGoogle Scholar
  28. 28.
    Aslani H, Yaghoobi M, Akbarzadeh-T MR (2015) Chaotic inertia weight in black hole algorithm for function optimization. In: International congress on technology, communication and knowledge (ICTCK) 2015. IEEE, pp 123–129Google Scholar
  29. 29.
    Yang NC, Le MD (2015) Multi-objective bat algorithm with time-varying inertia weights for optimal design of passive power filters set. IET Gener Transm Distrib 9(7):644–654CrossRefGoogle Scholar
  30. 30.
    Li G, Qi X, Chen B et al (2017) Fast learning network with parallel layer perceptrons. Neural Process Lett 6:1–16Google Scholar
  31. 31.
    Wang J, Wu W, Li Z, Li L (2011) Convergence of gradient method for double parallel feedforward neural network. Int J Numer Anal Model 8:484–495MathSciNetzbMATHGoogle Scholar
  32. 32.
    Glover B (2014) Understanding flowers and flowering. Oxford University Press, OxfordCrossRefGoogle Scholar
  33. 33.
    Łukasik S, Kowalski PA (2015) Study of flower pollination algorithm for continuous optimization. In: Angelov P, Atanassov KT, Doukovska L, Hadjiski M, Jotsov V, Kacprzyk J, Kasabov N, Sotirov S, Szmidt E, Zadrożny S (eds) Intelligent Systems’2014. Springer, Cham, pp 451–459Google Scholar
  34. 34.
    Pant S, Kumar A, Ram M (2017) Flower pollination algorithm development: a state of art review. Int J Syst Assur Eng Manag 2:1–9Google Scholar
  35. 35.
    Pavlyukevich I (2007) Lévy flights, non-local search and simulated annealing. J Comput Phys 226(2):1830–1844MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Nabil E (2016) A modified flower pollination algorithm for global optimization. Expert Syst Appl 57:192–203CrossRefGoogle Scholar
  37. 37.
    Zhou Y, Wang R, Luo Q (2016) Elite opposition-based flower pollination algorithm. Neurocomputing 188:294–310CrossRefGoogle Scholar
  38. 38.
    Ramadas M, Kumar S (2016) An efficient hybrid approach using differential evolution and flower pollination algorithm. In: 6th international conference on cloud system and big data engineering (Confluence). IEEE, pp 59–64Google Scholar
  39. 39.
    Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: The 1998 IEEE international conference on IEEE world congress on computational intelligence evolutionary computation proceedings. IEEE, pp 69–73Google Scholar
  40. 40.
    Ozcan E, Mohan CK (1998) Analysis of a simple particle swarm optimization system. Intell Eng Syst Through Artif Neural Netw 8:253–258Google Scholar
  41. 41.
    Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31CrossRefGoogle Scholar
  42. 42.
    Derrac J, García S, Molina D et al (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut Comput 1(1):3–18CrossRefGoogle Scholar
  43. 43.
    Song Z, Kusiak A (2007) Constraint-based control of boiler efficiency: a data-mining approach. IEEE Trans Industr Inf 3(1):73–83CrossRefGoogle Scholar
  44. 44.
    Li G, Niu P, Liu C et al (2012) Enhanced combination modeling method for combustion efficiency in coal-fired boilers. Appl Soft Comput J 12(10):3132–3140CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Lab of Industrial Computer Control Engineering of Hebei ProvinceYanshan UniversityQinhuangdaoChina
  2. 2.National Engineering Research Center for Equipment and Technology of Cold Strip RollingQinhuangdaoChina

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