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Quantum ant colony optimization algorithm for AGVs path planning based on Bloch coordinates of pheromones

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Abstract

In this work, a novel quantum ant colony optimization algorithm for automated guided vehicles (AGVs) path planning based on Bloch coordinates of pheromones is proposed. In consideration of the difficulty in solving the AGVs path planning problem because of NP-hard computational complexity, this approach combines the advantages of quantum theory and ant colony algorithm to obtain feasible, conflict-free, and optimal paths. To expand the search space, the pheromones on paths are coded according to Bloch coordinates. To make full use of the pheromones of three-dimensional Bloch coordinates, they are chosen with certain probabilities in accordance with the paths they obtained. Repulsions among AGVs are supposed to exist to avoid conflicts. A repulsion factor is employed in the state transition rule to increase the space–time distance among AGVs as much as possible. We compare the performance of the proposed algorithm with those of the other three methods in simulation of AGVs path planning at an automated container terminal. Simulation results illustrate the superiority of the proposed algorithm.

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Acknowledgements

This work is supported by the Ministry of Education of Humanities and Social Science Project (Nos. 15YJC630145, 15YJC630059), the Soft Science Research Key Project of Shanghai Science and Technology Innovation Action Plan (No. 18692105500). Here we would like to express our gratitude to them.

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Authors and Affiliations

  1. Merchant Marine College, Shanghai Maritime University, Shanghai, China

    Junjun Li & Huafeng Wu

  2. Institute of Logistics Science & Engineering, Shanghai Maritime University, Shanghai, China

    Bowei Xu & Yongsheng Yang

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  1. Junjun Li
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  2. Bowei Xu
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  3. Yongsheng Yang
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  4. Huafeng Wu
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Correspondence to Bowei Xu.

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Li, J., Xu, B., Yang, Y. et al. Quantum ant colony optimization algorithm for AGVs path planning based on Bloch coordinates of pheromones. Nat Comput 19, 673–682 (2020). https://doi.org/10.1007/s11047-018-9711-0

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  • DOI: https://doi.org/10.1007/s11047-018-9711-0

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