Abstract: Aiming at the problem that the traditional flower pollination algorithm (FPA) was significantly affected by initial parameters and prone to local optima or convergence failures, we proposed  a quantum-inspired flower pollination algorithm (QFPA). By incorporating quantum systems into the FPA,  the  pollination search process was made more efficient, thereby improving global search capabilities. Additionally, trajectory analysis was employed to better enable the population to escape from local optima and further reduce errors. In order to verify  the effectiveness of the method, firstly, the  QFPA was evaluated using selected benchmark functions. Secondly,  the best evaluated  QFPA was used  to optimize the hyperparameters of the long short-term memory network (LSTM) model. Finally, the experiments were conducted on an air quality dataset after removing noise  using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, and compared with several other commonly used optimization algorithms. The experimental results show that QFPA  enhances the global search capability and convergence properties of optimization algorithms. The QFPA-LSTM model improves the accuracy and efficiency of long-term time series predictions, with a root mean square error of 10.93 μg/m³, thus providing a reliable solution for air quality prediction in practical applications.

Key words: flower pollination algorithm, quantum-inspired flower pollination algorithm, CEEMDAN algorithm, LSTM model