Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm

A novel hybrid swarm intelligence algorithm for chaotic system parameter estimation is present. For this purpose, the parameters estimation on Lorenz systems is formulated as a multidimensional problem, and a hybrid approach based on particle swarm optimization with ant colony optimization (PSO–ACO) is implemented to solve this problem. Firstly, the performance of the proposed PSO–ACO algorithm is tested on a set of three representative benchmark functions, and the impact of the parameter settings on PSO–ACO efficiency is studied. Secondly, the parameter estimation is converted into an optimization problem on a three-dimensional Lorenz system. Numerical simulations on Lorenz model and comparisons with results obtained by other algorithms showed that PSO–ACO is a very powerful tool for parameter estimation with high accuracy and low deviations. •PSO–ACO combined particle swarm optimization with ant colony optimization.•This study is the first research of PSO–ACO to estimate parameters of chaotic systems.•PSO–ACO algorithm can identify the parameters of the three-dimensional Lorenz system with low deviations.•PSO–ACO is a very powerful tool for the parameter estimation on other chaotic system.