A Novel Flower Pollination Algorithm for Modeling the Boiler Thermal Efficiency

摘要

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.