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An Enhanced Ant Colony Algorithm for Vehicle Path Planning Optimization Problem

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Artificial Intelligence in Data and Big Data Processing (ICABDE 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 124))

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Abstract

When confronted with a complicated issue in a particular combinatorial situation, the ant colony optimization algorithm (ACO) can easily slip into optimal local solutions, poor convergence speed, and other drawbacks. This study suggests an enhanced version for ACO (EACO) using information guide period ants search matrix with varied search times and pheromone volatilization factors to achieve algorithm balance between “exploration” and “exploitation. The EACO applies for logistics vehicle path planning optimization (VPP) by using the optimal solution of an “opts” method. The experimental results reveal that the proposed algorithm outperforms the ACO and the other algorithms in the literature for logistical distribution paths and delivery vehicle routes to satisfy customers’ needs.

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Author information

Authors and Affiliations

  1. Fujian Provincial Key Lab of Big Data Mining and Applications, Fujian University of Technology Fujian, Fujian, China

    Thi-Kien Dao

  2. University of Information Technology, VNU-HCM, Ho Chi Minh City, Vietnam

    Trinh-Dong Nguyen & Trong-The Nguyen

  3. Vietnam National University, Ho Chi Minh City, Vietnam

    Trinh-Dong Nguyen & Trong-The Nguyen

  4. Faculty of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam

    Truong-Giang Ngo

  5. Institute of Information Technology, Academy of Science and Technology, Hanoi, Vietnam

    Quoc-Tao Ngo

Authors
  1. Thi-Kien Dao
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  2. Trinh-Dong Nguyen
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  3. Truong-Giang Ngo
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  4. Trong-The Nguyen
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  5. Quoc-Tao Ngo
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Corresponding author

Correspondence to Truong-Giang Ngo .

Editor information

Editors and Affiliations

  1. Department of Information Technology, College of Technology and Design, University of Economics Ho Chi Minh City (UEH), Ho Chi Minh City, Vietnam

    Ngoc Hoang Thanh Dang

  2. Department of Informatics, University of Leicester, Leicester, UK

    Yu-Dong Zhang

  3. Department of Mechanical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal

    João Manuel R. S. Tavares

  4. Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan

    Bo-Hao Chen

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Dao, TK., Nguyen, TD., Ngo, TG., Nguyen, TT., Ngo, QT. (2022). An Enhanced Ant Colony Algorithm for Vehicle Path Planning Optimization Problem. In: Dang, N.H.T., Zhang, YD., Tavares, J.M.R.S., Chen, BH. (eds) Artificial Intelligence in Data and Big Data Processing. ICABDE 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 124. Springer, Cham. https://doi.org/10.1007/978-3-030-97610-1_10

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