An effective hybrid quantum-inspired evolutionary algorithm for parameter estimation of chaotic systems

Parameter estimation of chaotic systems is an important issue and has attracted increasing interest from a variety of research fields. Recently, quantum-inspired evolutionary algorithms have been proposed and applied to some optimization problems. However, to the best of our knowledge, there is no published research work on quantum-inspired evolutionary algorithm (QEA) for estimating parameters of chaotic systems. In this paper, an effective hybrid quantum-inspired evolutionary algorithm with differential evolution (HQEDE) is proposed and applied to estimate the parameters of the Lorenz system. Numerical simulation and comparisons with other methods demonstrate the effectiveness and robustness of the proposed algorithm. In addition, the effects of the parameter settings on HQEDE are investigated.