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Improving Chaotic Ant Swarm Performance with Three Strategies

  • Yu-Ying Li
  • Li-Xiang Li
  • Hai-Peng Peng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7928)

Abstract

This paper presents an improved chaotic ant swarm (ICAS) by introducing three strategies, which are comprehensive learning strategy, search bound strategy and refinement search strategy, into chaotic ant swarm (CAS) for solving optimization problems. The first two strategies are employed to update ants’ positions, which preserve the diversity of the swarm so that the ICAS discourages premature convergence. In addition, the refinement search strategy is adopted to increase the solution quality in the ICAS. Simulations show that the ICAS significantly enhances solution accuracy and convergence stability of the CAS.

Keywords

Benchmark Function Chaotic Neural Network Good Exemplar Rastrigin Function Chaotic Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yu-Ying Li
    • 1
  • Li-Xiang Li
    • 2
  • Hai-Peng Peng
    • 2
  1. 1.Basis DepartmentInstitute of Chemical Defense of the Chinese People’s Liberation ArmyBeijingChina
  2. 2.Information Security CenterBeijing University of Posts and TelecommunicationsBeijingChina

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