基于混合生物地理优化的混沌系统参数估计

摘要

混沌系统的参数估计本质上是多维参数的优化问题。结合和声搜索方法和对立学习机理，提出一种混合生物地理优化算法，用于解决混沌系统的参数估计问题。利用对立学习机理增加初始群体的多样性，并引入和声搜索以增强局部寻优能力，从而提升整体寻优性能。以典型混沌系统为例进行了未知参数估计的数值仿真，结果验证了所提出混合生物地理优化方法的有效性和鲁棒性。%Parameter estimation for chaotic system is, in fact, a multi-dimensional optimization problem. By combining biogeography-based optimization (BBO) with harmony search (HS) and opposition-based learning (OBL), a hybrid BBO scheme is proposed for solving the chaotic parameter estimation problem. The HS is used to enhance the local search ability of BBO, and OBL is employed to increase the diversity of the initial population, thereby improving the optimizing performance. The effectiveness and robustness of the proposed scheme are verified by numerical simulations on two typical chaotic systems.