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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)

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.

Keywords

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

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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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