An effective hybrid biogeography-based optimization algorithm for parameter estimation of chaotic systems

Authors:
Ling Wang;Ye Xu
Affiliations:
Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, China;Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 4
Cited 1

Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Journal of Global Optimization
Parameter identification of chaotic systems using evolutionary programming approach

Expert Systems with Applications: An International Journal
An effective hybrid algorithm based on simplex search and differential evolution for global optimization

ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Biogeography-Based Optimization

IEEE Transactions on Evolutionary Computation

Original article: Exponential nonlinear observer for parametric identification and synchronization of chaotic systems

Mathematics and Computers in Simulation

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Abstract

Parameter estimation of chaotic systems is an important issue in the fields of computational mathematics and nonlinear science, which has gained increasing research and applications. In this paper, biogeography-based optimization (BBO), a new effective optimization algorithm based on the biogeography theory of the geographical distribution of biological organisms, is reasonably combined with differential evolution and simplex search to develop an effective hybrid algorithm for solving parameter estimation problem that is formulated as a multi-dimensional optimization problem. By suitably fusing several optimization methods with different searching mechanisms and features, the exploration and exploitation abilities of the hybrid algorithm can be enhanced and well balanced. Numerical simulation based on several typical chaotic systems and comparisons with some existing methods demonstrate the effectiveness of the proposed algorithm. In addition, the effects of population size and noise on the performances of the hybrid algorithm are investigated.