Information Entropy and Interaction Optimization Model Based on Swarm Intelligence

  • Xiaoxian He
  • Yunlong Zhu
  • Kunyuan Hu
  • Ben Niu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)


By introducing the information entropy H(X) and mutual information I(X;Y) of information theory into swarm intelligence, the Interaction Optimization Model (IOM) is proposed. In this model, the information interaction process of individuals is analyzed with H(X) and I(X;Y) aiming at solving optimization problems. We call this optimization approach as interaction optimization. In order to validate this model, we proposed a new algorithm for Traveling Salesman Problem (TSP), namely Route-Exchange Algorithm (REA), which is inspired by the information interaction of individuals in swarm intelligence. Some benchmarks are tested in the experiments. The results indicate that the algorithm can quickly converge to the optimal solution with quite low cost.


Particle Swarm Optimizer Mutual Information Travel Salesman Problem Travel Salesman Problem Information Entropy 
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 2006

Authors and Affiliations

  • Xiaoxian He
    • 1
    • 2
  • Yunlong Zhu
    • 1
  • Kunyuan Hu
    • 1
  • Ben Niu
    • 1
    • 2
  1. 1.Shenyang Institute of AutomationChinese Academy of SciencesShenyang
  2. 2.Graduate school of the Chinese Academy of SciencesBeijing

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