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Related Concept Videos

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Vector Functions and Motion: Problem Solving

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Related Experiment Videos

QLFDGWO: Q-Learning-Guided Weighted Fitness-Distance Grey Wolf Optimizer for UAV Path Planning.

Chen Huang1, Beining Yang1, Yan Huo2

  • 1College of Civil Aviation, Shenyang Aerospace University, Shenyang 110136, China.

Biomimetics (Basel, Switzerland)
|June 25, 2026
PubMed
Summary

This study introduces an improved Grey Wolf Optimizer (GWO) using Q-learning for better diversity and stability in complex tasks. The enhanced QLFDGWO algorithm shows superior performance in optimization and unmanned aerial vehicle (UAV) path planning.

Keywords:
Q-learningUAV path planningcosine nonlineargrey wolf optimization

Related Experiment Videos

Area of Science:

  • Optimization Algorithms
  • Artificial Intelligence
  • Computational Intelligence

Background:

  • Traditional Grey Wolf Optimizer (GWO) faces challenges like limited search diversity, unstable transitions, and premature convergence in complex optimization problems.
  • Addressing these limitations is crucial for developing more robust and efficient optimization techniques.

Purpose of the Study:

  • To propose an improved Grey Wolf Optimizer (GWO) integrated with a Q-learning-guided fitness-distance-weighted selector, termed QLFDGWO.
  • To enhance the search diversity, convergence speed, and stability of the GWO algorithm for complex optimization tasks.
  • To validate the effectiveness of QLFDGWO in both benchmark function optimization and three-dimensional (3D) unmanned aerial vehicle (UAV) path planning.

Main Methods:

  • Introduction of chaotic mapping for a more diverse initial population.
  • Utilization of a cosine nonlinear convergence factor to improve search adjustment capability.
  • Implementation of a Q-learning-based strategy selection mechanism for adaptive exploration-exploitation switching.
  • Design of a Q-learning-guided fitness-distance-weighted selection mechanism for improved leadership structure.
  • Application of a dynamic threshold-weighted update strategy to enhance convergence accuracy and stability.

Main Results:

  • QLFDGWO demonstrated superior performance compared to five representative optimization algorithms on the CEC2017 benchmark function set.
  • The algorithm achieved significant improvements in optimization accuracy, convergence speed, and robustness.
  • Simulations for 3D UAV path planning confirmed QLFDGWO's ability to generate feasible, safe paths under complex terrain and obstacle constraints.

Conclusions:

  • The proposed QLFDGWO framework effectively overcomes the limitations of the traditional GWO.
  • The integration of Q-learning and fitness-distance weighting significantly enhances optimization capabilities.
  • QLFDGWO shows practical applicability in complex real-world scenarios such as UAV path planning.