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A Novel Artificial Fish Swarm Algorithm Based on Multi-objective Optimization

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Intelligent Computing Theories and Applications (ICIC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7390))

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Abstract

As is well known, there isn’t exists only the global optimal solution making all objective functions are optimized in multi-objective optimization problem. In this paper, a novel global artificial fish swarm algorithm is proposed in order to finding the Pareto approximate solution of Mop. The chaotic search initialization and improved differential evolution methods were proposed to lead artificial fish into global optimum value. The experimental results show that the proposed algorithm is superior to traditional one and feasible for multi-objective optimization problem.

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References

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Author information

Authors and Affiliations

  1. College of Information and Engineering, Wuyi University, Jiangmen, Guangdong, 520020, China

    Yi-Kui Zhai, Ying Xu & Jun-Ying Gan

Authors
  1. Yi-Kui Zhai
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  2. Ying Xu
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  3. Jun-Ying Gan
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Editor information

Editors and Affiliations

  1. School of Electronics and Information Engineering, Machine Learning and Systems Biology Laboratory, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. Faculty of Computer and Information Sciences, Hosei University, 3-7-2, Kajino-Cho, Koganei-Shi, Japan

    Jianhua Ma

  3. School of Electrical Engineering, University of Ulsan, #7-413, San 29, Muger Dong, 680-749, Ulsan, South Korea

    Kang-Hyun Jo

  4. Department of Biotechnology, Indian Institute of Technology (IIT) Madras, 600 036, Chennai, Tamilnadu, India

    M. Michael Gromiha

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© 2012 Springer-Verlag Berlin Heidelberg

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Zhai, YK., Xu, Y., Gan, JY. (2012). A Novel Artificial Fish Swarm Algorithm Based on Multi-objective Optimization. In: Huang, DS., Ma, J., Jo, KH., Gromiha, M.M. (eds) Intelligent Computing Theories and Applications. ICIC 2012. Lecture Notes in Computer Science(), vol 7390. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31576-3_9

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  • DOI: https://doi.org/10.1007/978-3-642-31576-3_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31575-6

  • Online ISBN: 978-3-642-31576-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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