A Novel Flower Pollination Algorithm for Modeling the Boiler Thermal Efficiency

作者：Niu, Peifeng; Li, Jinbai^*; Chang, Lingfang; Zhang, Xianchen; Wang, Rongyan; Li, Guoqiang

来源：Neural Processing Letters, 2019, 49(2): 737-759.

DOI：10.1007/s11063-018-9854-0

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

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 330MW 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.

• 出版日期2019-4
• 单位燕山大学