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Application of Ant Colony Algorithms to Solve the Vehicle Routing Problem

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10954))

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Abstract

Many optimization problems exist in the world. The Vehicle Routing Problem (VRP) is a relatively complex and high-level issue. The ant colony algorithm has certain advantages for solving the capacity-based vehicle routing problem (CVRP), but is prone to local optimization and high search speed problems. To solve these problems, this paper proposes an adaptive hybrid ant colony optimization algorithm to solve the vehicle routing problem with larger capacity. The adaptive hybrid ant colony optimization algorithm uses a genetic algorithm to adjust the pheromone matrix algorithm, designs an adaptive pheromone evaporation rate adjustment strategy, and uses a local search strategy to reduce computation. Experiments on some classic problems show that the proposed algorithm is effective for solving vehicle routing problems and has good performance. In the experiment, the results of different scale issues were compared with previously published papers. Experimental results show that the algorithm is feasible and effective.

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Acknowledgement

This research is partially supported by National Science Foundation of China under Grant 61773192, 61773246, 61603169 and 61503170, Shandong Province Higher Educational Science and Technology Program (J17KZ005), Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education (K93-9-2017-02), and State Key Laboratory of Synthetical Automation for Process Industries (PAL-N201602).

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Authors and Affiliations

  1. School of Computer, Liaocheng University, Liaocheng, 252059, China

    Mei-xian Song, Jun-qing Li, Li Li & Wang Yong

  2. School of Information, Shandong Normal University, Jinan, 250014, China

    Jun-qing Li & Pei-yong Duan

  3. China Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing, 211189, People’s Republic of China

    Jun-qing Li

  4. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819, China

    Jun-qing Li

Authors
  1. Mei-xian Song
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  2. Jun-qing Li
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  3. Li Li
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  4. Wang Yong
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  5. Pei-yong Duan
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Corresponding author

Correspondence to Pei-yong Duan .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytechnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

  4. Indian Institute of Technology Kanpur, Kanpur, India

    Phalguni Gupta

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Song, Mx., Li, Jq., Li, L., Yong, W., Duan, Py. (2018). Application of Ant Colony Algorithms to Solve the Vehicle Routing Problem. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_83

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  • DOI: https://doi.org/10.1007/978-3-319-95930-6_83

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95929-0

  • Online ISBN: 978-3-319-95930-6

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