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Research on Vehicle Routing Planning Based on Adaptive Ant Colony and Particle Swarm Optimization Algorithm

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Abstract

Aiming at vehicle routing problem and combining the advantages of ant colony and particle swarm optimization, an intelligent optimization algorithm of adaptive ant colony and particle swarm optimization is proposed. Through the simulation of ant colony and bird swarm intelligence mechanism, the particle swarm algorithm and the ant colony algorithm heuristic strategy are combined, and different search strategies are used in different stages of the algorithm. The adaptive adjustment is adopted, and the feedback information is obtained by dynamic interaction with the environment, thus speeding up the convergence speed, improving the learning ability, avoiding the local optimum, getting the best solution and improving the efficiency. The simulation experiment shows that the algorithm has fast convergence speed, strong optimization ability, and can obtain better optimization results. It has some advantages in solving vehicle routing problem.

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Acknowledgments

The research in this paper was supported by Physical Science Fund Project of Universities and Colleges in Jiangsu Province: Research on Target Tracking in Wireless Sensor Networks Based on Particle Filter Optimization Algorithm (NO. 15KJB510004), and Scientific Research Project of the 13th Five-Year in 2016: Research on Location and Tracking Based on Adaptive Particle Swarm Optimization Algorithm (NO. 16SSW-Y-008).

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Authors and Affiliations

  1. Jiangsu Open University, Room 1203, No. 399, Jiangdong north Road, Nanjing, 210036, Jiangsu, China

    Chunyan Jiang, Jingfang Fu & Weiyan Liu

  2. The City Vocational College of Jiangsu, Nanjing, 210036, China

    Chunyan Jiang, Jingfang Fu & Weiyan Liu

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  1. Chunyan Jiang
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  2. Jingfang Fu
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Correspondence to Chunyan Jiang.

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Jiang, C., Fu, J. & Liu, W. Research on Vehicle Routing Planning Based on Adaptive Ant Colony and Particle Swarm Optimization Algorithm. Int. J. ITS Res. 19, 83–91 (2021). https://doi.org/10.1007/s13177-020-00224-3

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  • DOI: https://doi.org/10.1007/s13177-020-00224-3

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