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No Access Submitted: 31 January 2011 Accepted: 08 May 2011 Published Online: 28 June 2011

Multiobjective synchronization of coupled systems

Chaos 21, 025114 (2011); https://doi.org/10.1063/1.3595701
Yang Tang1, a), Zidong Wang2, b), W. K. Wong3, c), Jürgen Kurths4, d), and Jian-an Fang5
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  • 1School of Information Science and Technology, Donghua University, Shanghai, China and Potsdam Institute for Climate Impact Research, Potsdam, Germany
  • 2Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex, UB8 3PH, United Kingdom
  • 3Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong, China
  • 4Potsdam Institute for Climate Impact Research, Potsdam, Germany and Institute of Physics, Humboldt University, Berlin, Germany and Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen AB24 3UE, United Kingdom
  • 5Department of Automation, Donghua University, Shanghai, China

  • a)Also at Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong, China. Electronic mail: .

    b)Also at School of Information Science and Technology, Donghua University, Shanghai, China. Electronic mail: .

    c)Electronic mail: .

    d)Electronic mail: .

ABSTRACT
In this paper, multiobjective synchronization of chaotic systems is investigated by especially simultaneously minimizing optimization of control cost and convergence speed. The coupling form and coupling strength are optimized by an improved multiobjective evolutionary approach that includes a hybrid chromosome representation. The hybrid encoding scheme combines binary representation with real number representation. The constraints on the coupling form are also considered by converting the multiobjective synchronization into a multiobjective constraint problem. In addition, the performances of the adaptive learning method and non-dominated sorting genetic algorithm-II as well as the effectiveness and contributions of the proposed approach are analyzed and validated through the Rössler system in a chaotic or hyperchaotic regime and delayed chaotic neural networks.

ACKNOWLEDGMENTS

The authors would like to express their sincere appreciation to the editor and reviewers for their helpful comments which help improve the presentation and quality of the paper.
The works of Y. Tang and W. K. Wong were supported by a research grant from The Hong Kong Polytechnic University (Project No. G-YH11). The work of Y. Tang was also supported by the Alexander von Humboldt Foundation of Germany. The work of Z. D. Wang was supported by the Engineering and Physical Sciences Research Council EPSRC of the U. K. under Grant No. GR/S27658/01, an International Joint Project sponsored by the Royal Society of the U.K., the International Science and Technology Cooperation Project of China (Grant No. 2009DFA32050), and the Alexander von Humboldt Foundation of Germany. The work of J. Kurths was supported by SUMO (EU) and ECONS (WGL). The work of J. A. Fang was supported by the National Natural Science Foundation of PR China (Grant No. 60874113), the Research Fund for the Doctoral Program of Higher Education (Grant No. 200802550007), the Key Creative Project of Shanghai Education Community (Grant No. 09ZZ66), the Key Foundation Project of Shanghai (Grant No. 09JC1400700).

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  1. © 2011 American Institute of Physics.

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