Enhanced global flower pollination algorithm for parameter identification of chaotic and hyper-chaotic system

The problem of system parameter identification is a fundamental problem in the field of nonlinear science, which can be described as a multidimensional optimization problem. In this paper, an enhanced global flower pollination algorithm (GFPA) is proposed for parameter identification of chaotic and hyper-chaotic systems. The motion trajectory of the flower pollination algorithm is analyzed for the first time, and the equation of the algorithm exploration phase is improved by the chaotic mapping method to ensure the convergence of the algorithm in the exploration phase. In addition, in order to improve the convergence speed of the algorithm, the update method of the exploitation phase is reset by using the best information to guide the searching. Through analysis, the proposed new algorithm can guarantee the convergence of the algorithm without increasing the time complexity. Finally, we identify and validate the system of the Lorenz, Rossler, Chen and the system of the Rossler hyper-chaotic, Chen hyper-chaotic. The experimental results show that GFPA has better identification effect.