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A Memetic-GRASP Algorithm for the Solution of the Orienteering Problem

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Modelling, Computation and Optimization in Information Systems and Management Sciences

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 360))

Abstract

The last decade a large number of applications in logistics, tourism and other fields have been studied and modeled as Orienteering Problems (OPs). In the orienteering problem, a standard amount of nodes are given, each with a specific score. The goal is to determine a path, limited in length, from the start point to the end point through a subset of locations in order to maximize the total path score. In this paper, we present a new hybrid evolutionary algorithm for the solution of the Orienteering Problem. The algorithm combines a Greedy Randomized Adaptive Search Procedure (GRASP), an Evolutionary Algorithm and two local search procedures. The algorithm was tested in a number of benchmark instances from the literature and in most of them the best known solutions were found.

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Authors and Affiliations

  1. School of Production Engineering and Management, Technical University of Crete, Chania, Greece

    Yannis Marinakis, Michael Politis, Magdalene Marinaki & Nikolaos Matsatsinis

Authors
  1. Yannis Marinakis
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  2. Michael Politis
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  3. Magdalene Marinaki
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  4. Nikolaos Matsatsinis
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Corresponding author

Correspondence to Yannis Marinakis .

Editor information

Editors and Affiliations

  1. Laboratory of Theoretical and Applied Computer Science, University of Lorraine, Metz, France

    Hoai An Le Thi

  2. Laboratory of Mathematics, National Institute for Applied Sciences, Rouen, France

    Tao Pham Dinh

  3. Division of Knowledge Management Systems, Wroclaw University of Technology, Poland

    Ngoc Thanh Nguyen

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© 2015 Springer International Publishing Switzerland

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Marinakis, Y., Politis, M., Marinaki, M., Matsatsinis, N. (2015). A Memetic-GRASP Algorithm for the Solution of the Orienteering Problem. In: Le Thi, H., Pham Dinh, T., Nguyen, N. (eds) Modelling, Computation and Optimization in Information Systems and Management Sciences. Advances in Intelligent Systems and Computing, vol 360. Springer, Cham. https://doi.org/10.1007/978-3-319-18167-7_10

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  • DOI: https://doi.org/10.1007/978-3-319-18167-7_10

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18166-0

  • Online ISBN: 978-3-319-18167-7

  • eBook Packages: EngineeringEngineering (R0)

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