Parameter estimation for chaotic systems by improved artificial bee colony algorithm

摘要

This paper is concerned with the parameter estimation of nonlinear chaotic system, which could be essentially formulated as a multi-dimension optimization problem. In this article, an improved artificial bee colony algorithm is implemented to solve parameter estimation for chaotic systems. This algorithm can combine the stochastic exploration of the artificial bee colony and the exploitation capability of new search strategies. Experiments have been conducted on Lorenz system and Chen system. The proposed algorithm is applied to estimate the parameters of these two systems. Simulation results and comparisons demonstrate that the proposed algorithm is better or at least comparable to drift particle swarm optimization, particle swarm optimization and genetic algorithm from literature when considering the quality of the solutions obtained.