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A CPU-GPU Parallel Ant Colony Optimization Solver for the Vehicle Routing Problem

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Applications of Evolutionary Computation (EvoApplications 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10784))

Abstract

This paper exposes a new hybrid approach based on Ant Colony Optimization heuristics, Route First-Cluster Second methods and Local search procedures, combined to generate high quality solutions for the Vehicle Routing Problem. This method uses the parallel computing power of modern general purpose GPUs and multicore CPUs, outperforming current ACO-based VRP solvers and showing to be a competitive approach compared to other high performing metaheuristic solvers.

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Acknowledgements

This work has been supported by the EU (FEDER) and the Spanish MINECO, under grants TIN2014-54806-R, TIN2015-65277-R and BES-2016-076806.

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Authors and Affiliations

  1. Departamento de Arquitectura de Computadores y Automática, Facultad de Informática, Universidad Complutense de Madrid, Prof. J. G. Santesmases, 9, 28040, Madrid, Spain

    Antón Rey, Manuel Prieto, J. I. Gómez, Christian Tenllado & J. Ignacio Hidalgo

Authors
  1. Antón Rey
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  2. Manuel Prieto
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  3. J. I. Gómez
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  4. Christian Tenllado
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  5. J. Ignacio Hidalgo
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Corresponding author

Correspondence to Antón Rey .

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Editors and Affiliations

  1. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

  2. Johannes Gutenberg University of Mainz, Mainz, Germany

    Paul Kaufmann

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Rey, A., Prieto, M., Gómez, J.I., Tenllado, C., Hidalgo, J.I. (2018). A CPU-GPU Parallel Ant Colony Optimization Solver for the Vehicle Routing Problem. In: Sim, K., Kaufmann, P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science(), vol 10784. Springer, Cham. https://doi.org/10.1007/978-3-319-77538-8_44

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  • DOI: https://doi.org/10.1007/978-3-319-77538-8_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77537-1

  • Online ISBN: 978-3-319-77538-8

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