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The paper presents a diversity-based variable adjustment particle swarm optimization (DVAPSO) algorithm for path planning of multi-unmanned aerial vehicle (UAV) systems. Unlike conventional PSO-based methods that often suffer from premature convergence or require hybrid strategies with increased computational overhead, DVAPSO introduces a unified adaptation mechanism to dynamically regulate inertia weight and learning coefficients according to real-time population diversity. This mechanism enables the swarm to maintain high exploration capability during early iterations and reinforce convergence accuracy in later stages, effectively balancing global search and local refinement. Finally, the effectiveness of DVAPSO is verified through the MATLAB simulation.

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Diversity-Based Variable Adjustment Particle Swarm Optimization for Path Planning of Multi-UAV Systems

  • 会议论文

  • 发表日期：

    2026年

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  • Lingtong Kong,
  • Zhonghua Miao,
  • Jin Zhou

摘要

The paper presents a diversity-based variable adjustment particle swarm optimization (DVAPSO) algorithm for path planning of multi-unmanned aerial vehicle (UAV) systems. Unlike conventional PSO-based methods that often suffer from premature convergence or require hybrid strategies with increased computational overhead, DVAPSO introduces a unified adaptation mechanism to dynamically regulate inertia weight and learning coefficients according to real-time population diversity. This mechanism enables the swarm to maintain high exploration capability during early iterations and reinforce convergence accuracy in later stages, effectively balancing global search and local refinement. Finally, the effectiveness of DVAPSO is verified through the MATLAB simulation.