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Dynamic routing with ant system and memory-based decision-making process

Author

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  • Sina Abolhoseini

    (K. N. Toosi University of Technology)

  • Ali Asghar Alesheikh

    (K. N. Toosi University of Technology)

Abstract

Dynamic routing is an essential tool for today’s cities. Dynamic routing problems can be solved by modelling them as dynamic optimization problems (DOPs). DOPs can be solved using Swarm Intelligence and specially ant colony optimization (ACO) algorithms. Although different versions of ACO have already been presented for DOPs, there are still limitations in preventing stagnation and premature convergence and increasing convergence rate. To address these issues, we present an in-memory pheromone trail and an algorithm based on it (named AS-gamma) in the framework of ACO. In-memory pheromone trail is effectively increasing diversity after a change in an environment. Results of experimenting AS-gamma in three scenarios on a real-world transportation network with different simulated traffic conditions demonstrated the effectiveness of the presented in-memory pheromone trail method. The advantages of AS-gamma over three existing DOP algorithms have been illustrated in terms of solutions quality. Offline performance and accuracy measures indicate that AS-gamma faces less stagnation, premature convergence and it is suitable for crowded environments.

Suggested Citation

  • Sina Abolhoseini & Ali Asghar Alesheikh, 2021. "Dynamic routing with ant system and memory-based decision-making process," Environment Systems and Decisions, Springer, vol. 41(2), pages 198-211, June.
    • Handle: RePEc:spr:envsyd:v:41:y:2021:i:2:d:10.1007_s10669-020-09788-7
    DOI: 10.1007/s10669-020-09788-7
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    References listed on IDEAS

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    1. Haitao Xu & Pan Pu & Feng Duan, 2018. "Dynamic Vehicle Routing Problems with Enhanced Ant Colony Optimization," Discrete Dynamics in Nature and Society, Hindawi, vol. 2018, pages 1-13, February.
    2. Gao, Shangce & Wang, Yirui & Cheng, Jiujun & Inazumi, Yasuhiro & Tang, Zheng, 2016. "Ant colony optimization with clustering for solving the dynamic location routing problem," Applied Mathematics and Computation, Elsevier, vol. 285(C), pages 149-173.
    3. R. Montemanni & L. M. Gambardella & A. E. Rizzoli & A. V. Donati, 2005. "Ant Colony System for a Dynamic Vehicle Routing Problem," Journal of Combinatorial Optimization, Springer, vol. 10(4), pages 327-343, December.
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    Cited by:

    1. Zachary A. Collier & James H. Lambert & Igor Linkov, 2021. "Algorithms and models for decision making in advanced technology systems," Environment Systems and Decisions, Springer, vol. 41(2), pages 179-180, June.

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