Abstract
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.
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