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A Novel Flower Pollination Algorithm for Modeling the Boiler Thermal Efficiency

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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.

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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.

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Authors and Affiliations

  1. Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, 066004, China

    Peifeng Niu, Jinbai Li, Lingfang Chang, Xianchen Zhang, Rongyan Wang & Guoqiang Li

  2. National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Qinhuangdao, 066004, China

    Peifeng Niu, Jinbai Li, Lingfang Chang, Xianchen Zhang, Rongyan Wang & Guoqiang Li

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  1. Peifeng Niu
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  2. Jinbai Li
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  3. Lingfang Chang
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  4. Xianchen Zhang
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  5. Rongyan Wang
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  6. Guoqiang Li
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Correspondence to Jinbai Li.

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Niu, P., Li, J., Chang, L. et al. A Novel Flower Pollination Algorithm for Modeling the Boiler Thermal Efficiency. Neural Process Lett 49, 737–759 (2019). https://doi.org/10.1007/s11063-018-9854-0

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