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

  • Yi-Kui Zhai
  • Ying Xu
  • Jun-Ying Gan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7390)


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.


artificial fish swarm algorithm multi-objective optimization global optimum chaotic search 


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  1. 1.
    Li, X.L., Shao, Z.J., Qian, J.X.: An Optimizing Method Based on Autonomous Ani-mats: Fish-swarm Algorithm. Systems Engineering Theory & Practice (22), 32–38 (2002)Google Scholar
  2. 2.
    Aosyezka, M.: Optimization for Engineering Design. In: Gero, J. (ed.) Design Optimization, pp. 193–122. AcademicPress (1995)Google Scholar
  3. 3.
    Tan, Y., Tan, G.Z., Li, T.: Novel Chaos Differential Evolution Algorithm. Computer Engineering 35(11), 216–217 (2009)MathSciNetGoogle Scholar
  4. 4.
    Chu, X.L., Zhu, Y., Shi, J.T.: Image Edge Detection Based on Improved Artificial Fish-School Swarm Algorithm. Computer System and Application 19(8), 173–176 (2010)Google Scholar
  5. 5.
    Li, B., Jiang, W.S.: Chaos Optimization Method and Its Application. Control Theory and Applications 4, 613–615 (1997)Google Scholar
  6. 6.
    Zitzler, E., Deb, K., Thiele, L.: Comparison of Multi-objeetive Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8(2), 173–195 (2000)CrossRefGoogle Scholar
  7. 7.
    Fonseca, C.M., Fleming, P.J.: Genetic Algorithm for Multi-Objective Optimization: Formulation, Discussion and Generation. In: Proc of the 5th International Conference on Ge-netic Algorithms, Urbana-Champaign, USA, pp. 416–423 (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yi-Kui Zhai
    • 1
  • Ying Xu
    • 1
  • Jun-Ying Gan
    • 1
  1. 1.College of Information and EngineeringWuyi UniversityJiangmenChina

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