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Gaussian Guided Self-Adaptive Wolf Search Algorithm Based on Information Entropy Theory.

Qun Song1, Simon Fong1, Suash Deb2

  • 1Department of Computer and Information Science, University of Macau, Macau 999078, China.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

The Gaussian-Guided Self-Adaptive Wolf Search Algorithm (GSAWSA) enhances swarm intelligence by improving parameter control. This new method boosts performance for complex optimization problems compared to standard algorithms.

Keywords:
entropy-guided parameter controlself-adaptationswarm intelligence algorithmswolf search algorithm

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Swarm intelligence

Background:

  • Swarm intelligence algorithms are effective for optimization problems.
  • The Wolf Search Algorithm (WSA) excels at large-scale problems but is sensitive to parameter settings.
  • Previous self-adaptive methods for WSA were unstable.

Purpose of the Study:

  • To enhance the Self-Adaptive Wolf Search Algorithm (SAWSA) by improving its parameter control mechanism.
  • To investigate the search behavior of WSA more deeply.
  • To introduce a novel Gaussian-guided parameter updater based on information entropy theory.

Main Methods:

  • Development of a Gaussian-guided parameter control mechanism.
  • Integration of information entropy theory into the parameter updating function.
  • Simulation experiments comparing the new Gaussian-Guided Self-Adaptive Wolf Search Algorithm (GSAWSA) with existing methods.

Main Results:

  • The GSAWSA demonstrates significantly improved performance over the standard WSA.
  • The enhanced heuristic updating function leads to more stable and effective parameter adaptation.
  • Comparative analysis shows superior results against other prevalent swarm algorithms.

Conclusions:

  • The proposed Gaussian-guided parameter control mechanism effectively enhances WSA performance.
  • GSAWSA offers a more robust and efficient solution for complex optimization tasks.
  • This work advances the field of adaptive swarm intelligence algorithms.