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Warehouse Logistics AGV Path Planning Based on the Improved Artificial Bee Colony Algorithm

Author

Listed:
  • Hang Meng

    (Beijing Information Science and Technology University)

  • Chunyu Xing

    (Beijing Information Science and Technology University)

  • Haoran Yang

    (Beijing Information Science and Technology University)

  • Xiaorui Li

    (Beijing Information Science and Technology University)

Abstract

To achieve optimal path planning for Automated Guided Vehicles (AGV) in complex and dynamic warehousing environments, an improved artificial bee colony algorithm is proposed. This improvement involves introducing an adaptive k-nearest neighbor search strategy to enhance the search strategy of the original algorithm, utilizing feasible solutions outside the neighborhood and the global optimum to improve the selection strategy, and adjusting the guiding velocity of the global optimum with a dynamic factor β. These enhancements address issues such as premature convergence and low search efficiency in traditional artificial bee colony algorithms. Finally, simulation experiments are conducted using MATLAB software. The simulation results demonstrate that the proposed improved artificial bee colony algorithm is feasible and effective for path planning in warehousing environments.

Suggested Citation

  • Hang Meng & Chunyu Xing & Haoran Yang & Xiaorui Li, 2025. "Warehouse Logistics AGV Path Planning Based on the Improved Artificial Bee Colony Algorithm," Lecture Notes in Operations Research,, Springer.
    • Handle: RePEc:spr:lnopch:978-981-96-9697-0_53
    DOI: 10.1007/978-981-96-9697-0_53
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