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Hybrid Particle Swarm Optimization Based on Thermodynamic Mechanism

  • Yu Wu
  • Yuanxiang Li
  • Xing Xu
  • Sheng Peng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5361)

Abstract

This paper describes a thermodynamic particle swarm optimizer (TDPSO) based on the simple evolutionary equations. Inspired by the minimum free energy principle of the thermodynamic theoretics, a rating-based entropy (RE) and a component thermodynamic replacement (CTR) rule are implemented in the novel algorithm TDPSO. The concept of RE is utilized to systemically measure the fitness dispersal of the swarm with low computational cost. And the fitness range of all particles is divided into several ranks. Furthermore, the rule CTR is applied to control the optimal process with steeply fast convergence speed. It has the potential to maintain population diversity. Compared with the other improved PSO techniques, experimental results on some typical minimization problems show that the proposed technique outperforms other algorithms in terms of convergence speed and stability.

Keywords

particle swarm optimizer thermodynamic entropy swarm diversity replacement rule 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yu Wu
    • 1
  • Yuanxiang Li
    • 1
  • Xing Xu
    • 1
  • Sheng Peng
    • 1
  1. 1.State Key Lab. of Software EngineeringWuhan UniversityWuhanChina

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