Journal of Global Optimization
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A Hybrid Quantum-Inspired Genetic Algorithm for Multiobjective Flow Shop Scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hybrid computing scheme for shape optimisation in thermo-fluid problems
International Journal of Computational Intelligence Studies
Quantum-inspired evolutionary algorithms: a survey and empirical study
Journal of Heuristics
Global optimization of an optical chaotic system by Chaotic Multi Swarm Particle Swarm Optimization
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
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.