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Self-Adaptive Chaos Differential Evolution

  • Guo Zhenyu
  • Cheng Bo
  • Ye Min
  • Cao Binggang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)

Abstract

Differential evolution (DE) is a recently invented global optimization algorithm. In this paper, a new differential evolution algorithm, self-adaptive chaos differential evolution (SACDE) with chaos mutation factor and dynamically changing weighting factor and crossover factor is presented. The evolution speed factor and aggregation degree factor of the population are introduced in this new algorithm. In each iteration process, the weighting factor is changed dynamically based on the current aggregation degree factor, and the crossover factor is changed dynamically based on the current evolution speed factor. The chaos mutation factor is introduced to avoid falling into the local optimum. DE and SACDE are tested with two well-known benchmark functions. The experiments show that the convergence speed of SACDE is significantly superior to DE, the convergence accuracy is also increased.

Keywords

Differential Evolution Failure Time Convergence Speed Differential Evolution Algorithm Iteration Time 
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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References

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    Xuanping, Z., Yuping, D.: Adaptive Particle Swarm Algorithm with Dynamically Changing Intertia Weight. Journal of Xi’an Jiaotong University 39(10), 1039–1042 (2005)MATHGoogle Scholar
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    Zifa, L.: Optimal Reactive Power Dispatch Using Chaotic Particle Swarm Optimization Algorithm. Automation of Electric Power Systems 29(7), 53–57 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Guo Zhenyu
    • 1
  • Cheng Bo
    • 1
  • Ye Min
    • 1
  • Cao Binggang
    • 1
  1. 1.Research & Development Center of Electric VehicleXi’an Jiaotong UniversityXi’anChina

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