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Vehicle Path Optimization with Time Window Based on Improved Ant Colony Algorithm

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Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1088))

Abstract

Ant colony optimization (ACO) is a new heuristic algorithm developed by simulating ant foraging on the basis of group cooperative learning. TSP and other combinatorial optimization problems have been successfully solved. Like other heuristic search algorithms, ant colony algorithm has the disadvantage of being easily limited to local optimum. Aiming at the vehicle routing problem based on time window, the upper and lower limits of pheromone trajectory intensity are determined by analyzing the ant colony algorithm, and the transmission probability and pheromone updating method are improved to improve the convergence speed and global search ability of the algorithm. Aiming at the vehicle routing problem with time windows in logistics distribution, an improved maximum and minimum ant colony algorithm is proposed to improve the optimization performance. The algorithm can be extended to such related path optimization problems and applied.

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Acknowledgements

This work was supported by Natural Science Foundation of China (No. 61863013), Key R & D projects of Jiangxi science and Technology Department of China (No. 20161BBE50091), Science and Technology Foundation of Jiangxi Educational Committee of China (No. 150529), and East China Jiaotong University School Foundation Fund “Research on Urban Fire Monitoring System Based on IoT Collaboration Perception” (15RJ01).

Author information

Authors and Affiliations

  1. Information Engineering School, East China Jiaotong University, Nanchang, 330013, Jiangxi, China

    Bo Li

  2. Software School, East China Jiaotong University, Nanchang, 330013, Jiangxi, China

    Tingting Li

Authors
  1. Bo Li
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  2. Tingting Li
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Corresponding author

Correspondence to Bo Li .

Editor information

Editors and Affiliations

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

    Chuanchao Huang

  2. Computer Science and Information Management, Providence University, Taichung, Taiwan

    Yu-Wei Chan

  3. University of Aizu, Fukushima, Japan

    Neil Yen

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Li, B., Li, T. (2020). Vehicle Path Optimization with Time Window Based on Improved Ant Colony Algorithm. In: Huang, C., Chan, YW., Yen, N. (eds) Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019). Advances in Intelligent Systems and Computing, vol 1088. Springer, Singapore. https://doi.org/10.1007/978-981-15-1468-5_22

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