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Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest

Jiangnan Zhang1, Kewen Xia1, Ziping He1

  • 1School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, China.

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This study introduces an improved bird swarm algorithm (BSA) with dynamic multi-swarm, differential evolution, and quantum strategies. The enhanced BSA optimizes random forest models for more accurate and efficient oil logging predictions.

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Area of Science:

  • Artificial Intelligence
  • Swarm Intelligence
  • Machine Learning

Background:

  • The standard bird swarm algorithm (BSA) suffers from local optima and slow convergence.
  • Enhancing swarm intelligence algorithms is crucial for complex optimization tasks.

Purpose of the Study:

  • To address the limitations of the original BSA.
  • To develop a novel hybrid algorithm for improved optimization performance.
  • To enhance the accuracy and efficiency of random forest classification models.

Main Methods:

  • A dynamic multi-swarm strategy was integrated with differential evolution for enhanced global exploration.
  • Quantum behavior was introduced to improve search efficiency within the solution space.
  • The hybrid algorithm was applied to optimize random forest parameters (decision trees, predictor variables).

Main Results:

  • The improved BSA demonstrated superior performance on benchmark functions and UCI datasets compared to existing algorithms.
  • The optimized random forest model achieved higher accuracy and efficiency in oil logging prediction.
  • Experimental results validated the effectiveness of the three hybrid strategies.

Conclusions:

  • The proposed dynamic multi-swarm differential learning quantum bird swarm algorithm significantly enhances BSA performance.
  • The optimized random forest model provides a stable and accurate solution for oil logging prediction.
  • This research offers a competitive and efficient approach for complex classification and prediction tasks.