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This paper proposes an improved particle swarm optimization algorithm that enhances the standard particle swarm optimization by incorporating several key modifications, including a restructured velocity update mechanism and a novel stagnation-triggered adaptive optimization strategy. These improvements enhance the algorithm's ability to explore the search space and converge to near-optimal solutions more effectively. Experimental results conducted on the Solomon benchmark dataset demonstrate that the improved particle swarm optimization algorithm exhibits superior capability in solving the vehicle routing problem with time windows.

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An Improved Particle Swarm Optimization Algorithm for Vehicle Routing Problem with Time Windows

  • 会议论文

  • 发表日期：

    2026年

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  • Wenjie Yi,
  • Ying Chen,
  • Xinru He,
  • Gustave Florentin Nkoulou Mvondo,
  • Xusheng Wu

摘要

This paper proposes an improved particle swarm optimization algorithm that enhances the standard particle swarm optimization by incorporating several key modifications, including a restructured velocity update mechanism and a novel stagnation-triggered adaptive optimization strategy. These improvements enhance the algorithm's ability to explore the search space and converge to near-optimal solutions more effectively. Experimental results conducted on the Solomon benchmark dataset demonstrate that the improved particle swarm optimization algorithm exhibits superior capability in solving the vehicle routing problem with time windows.