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A Novel Hybrid Meta-Heuristic Algorithm Based on the Cross-Entropy Method and Firefly Algorithm for Global

Guocheng Li1,2, Pei Liu3, Chengyi Le4

  • 1School of Finance and Mathematics, West Anhui University, Lu'an 237012, China.

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

This study introduces a hybrid meta-heuristic algorithm combining the firefly algorithm (FA) with the cross-entropy (CE) method. The novel approach enhances global optimization capabilities, precision, and robustness for complex problems.

Keywords:
co-evolutioncross-entropy methodfirefly algorithmglobal optimizationmeta-heuristic

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Meta-heuristic methods

Background:

  • Global optimization presents significant challenges due to nonlinearity and multimodality, particularly at large scales.
  • Existing algorithms like the firefly algorithm (FA) can struggle with local optima and limited global searching ability.

Purpose of the Study:

  • To propose a novel hybrid meta-heuristic algorithm that enhances the global search capacity of the firefly algorithm.
  • To improve the balance between exploration and exploitation in optimization processes.
  • To address the challenges of nonlinearity and multimodality in large-scale global optimization problems.

Main Methods:

  • Embedding the cross-entropy (CE) method into the firefly algorithm (FA) to create a hybrid approach.
  • Utilizing adaptive smoothing and co-evolutionary strategies from the CE method.
  • Leveraging the ergodicity, adaptability, and robustness of the CE method within the FA framework.

Main Results:

  • The hybrid algorithm demonstrates a superior balance between exploration and exploitation, effectively avoiding local optima.
  • Significant enhancement in global searching ability and convergence rate compared to the standard FA.
  • Numerical experiments confirm more powerful global search capacity, higher optimization precision, and stronger robustness.

Conclusions:

  • The proposed hybrid meta-heuristic algorithm effectively overcomes the limitations of traditional methods for large-scale global optimization.
  • The integration of the cross-entropy method significantly boosts the performance of the firefly algorithm.
  • This novel approach offers a robust and precise solution for complex optimization tasks.