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International Conference on Bio-Inspired Computing: Theories and Applications

BIC-TA 2016: Bio-inspired Computing – Theories and Applications pp 204–218Cite as

Application of Discrete Ant Colony Optimization in VRPTW

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 682))

Abstract

The classical ant colony algorithm for vehicle routing problem with time windows (VRPTW) has problems of low efficiency, slow convergence and prematurity. And the discrete ant colony optimization (DACO) is proposed for these problem. It adopts the one-dimensional discrete coding that can make the data structure simpler and bring in faster convergence speed. In addition, self-convergence mode is used to calculate the optimal vehicle number rather than setting the optimal vehicle number at the beginning, which makes the algorithm more flexible and accelerates the convergence speed effectively. The time window and vehicle load are not considered in the optimization process, when ants complete the whole process and then the path is explained, this move not only expands the ant search scope but also improves the efficiency of the algorithm. The above highlights make the most of the self-adaptation and self-regulating mechanism, which effectively reduces the probability of the local optimal solution at the same time. Experimental results for Solomon benchmark test problems indicate that DACO outperforms both in reducing time and space complexity in the premise of not affecting the accuracy. Thus proves DACO is effective and feasible in solving the VRPTW.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant No. 61179032), the Special Scientific Research Fund of Food Public Welfare Profession of China (Grant No. 201513004-3) and the Research and Practice Project of Graduate Education Teaching Reform of Wuhan Polytechnic University (YZ2015002).

Author information

Authors and Affiliations

  1. School of Math and Computer, Wuhan Polytechnic University, Wuhan, 430023, Hubei, China

    Qinhong Fu

  2. Department of Economics and Management, Wuhan Polytechnic University, Wuhan, 430023, Hubei, China

    Kang Zhou & Huaqing Qi

  3. Key Laboratory of Image Information Processing and Intelligent Control, School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China

    Tingfang Wu

Authors
  1. Qinhong Fu
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  2. Kang Zhou
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  3. Huaqing Qi
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  4. Tingfang Wu
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Corresponding author

Correspondence to Kang Zhou .

Editor information

Editors and Affiliations

  1. Xidian University , Xi’an, China

    Maoguo Gong

  2. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  3. China University of Petroleum, Qingdao, China

    Tao Song

  4. Southwest Jiaotong University , Chengdu, China

    Gexiang Zhang

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© 2016 Springer Nature Singapore Pte Ltd.

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Fu, Q., Zhou, K., Qi, H., Wu, T. (2016). Application of Discrete Ant Colony Optimization in VRPTW. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_26

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  • DOI: https://doi.org/10.1007/978-981-10-3614-9_26

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3613-2

  • Online ISBN: 978-981-10-3614-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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