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Applying the Population-Based Ant Colony Optimization to the Dynamic Vehicle Routing Problem

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Advances in Swarm Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1054))

Abstract

The population-based ant colony optimization (P-ACO) algorithm is a variant of the ant colony optimization metaheuristic specifically designed to address dynamic optimization problems. Whenever a change in the environment occurs, P-ACO repairs the pheromone trails affected by the change using previous solutions maintained in a population-list. Typically, change-related information are utilized for repairing these solutions. The change-related information for this dynamic vehicle routing problem (DVRP) case are the nodes removed and inserted when a change in the environment occurs. In this chapter, the operators of the unstringing and stringing (US) heuristic are utilized for repairing the solutions. Experimental results demonstrate that P-ACO embedded with the US heuristic outperforms other peer methods in a series of DVRP test cases.

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Notes

  1. 1.

    Available from http://vrp.galgos.inf.puc-rio.br/index.php/en/.

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Acknowledgements

This work was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 739551 (KIOS CoE—TEAMING) and from the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy.

Author information

Authors and Affiliations

  1. KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus

    Michalis Mavrovouniotis, Georgios Ellinas & Marios Polycarpou

  2. Department of Applied Computing, Federal University of Santa Maria, 97105-900, Santa Maria, Brazil

    Felipe M. Müller

  3. PPGEP, Federal University of Santa Maria, 97105-900, Santa Maria, Brazil

    Iaê S. Bonilha

Authors
  1. Michalis Mavrovouniotis
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  2. Georgios Ellinas
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  3. Iaê S. Bonilha
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  4. Felipe M. Müller
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  5. Marios Polycarpou
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Corresponding author

Correspondence to Michalis Mavrovouniotis .

Editor information

Editors and Affiliations

  1. Department of CS and Engineering, National Institute Of Technology Silchar, Cachar, Assam, India

    Anupam Biswas

  2. Department of Industrial Engineering, Pamukkale Üniversitesi Hastaneleri, Pamukkale, Turkey

    Can B. Kalayci

  3. Centre for AI Research and Optimisation, Torrens University Australia, Brisbane, QLD, Australia

    Seyedali Mirjalili

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Mavrovouniotis, M., Ellinas, G., Bonilha, I.S., Müller, F.M., Polycarpou, M. (2023). Applying the Population-Based Ant Colony Optimization to the Dynamic Vehicle Routing Problem. In: Biswas, A., Kalayci, C.B., Mirjalili, S. (eds) Advances in Swarm Intelligence. Studies in Computational Intelligence, vol 1054. Springer, Cham. https://doi.org/10.1007/978-3-031-09835-2_20

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