skip to main content
10.1145/1389095.1389243acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
poster

Speed-up techniques for solving large-scale bTSP with the Two-Phase Pareto Local Search

Published: 12 July 2008 Publication History

Abstract

We first present a method, called Two-Phase Pareto Local Search, to find a good approximation of the efficient set of the biobjective traveling salesman problem. In the first phase of the method, an initial population composed of an approximation of the extreme supported efficient solutions is generated. We use as second phase a Pareto Local Search method applied to all solutions of the initial population. We show that using the combination of these two techniques: good initial population generation and Pareto Local Search gives good results, without numerical parameters. As the computational time of the second phase grows exponentially according to the instances size, speed-up techniques are used to considerably reduce the computational time of the second phase. It makes it possible to find good approximation of the efficient set of large-scale biobjective traveling salesman problems, in a reasonable resolution time.

References

[1]
Y. P. Aneja and K. P. K. Nair. Bicriteria transportation problem. Management Science, 25:73--78, 1979.
[2]
E. Angel, E. Bampis, and L. Gourvès. A dynasearch neighborhood for the bicriteria traveling salesman problem. In X. Gandibleux, M. Sevaux, K. Sörensen, and V. T'kindt, editors, Metaheuristics for Multiobjective Optimisation, pages 153--176. Springer. Lecture Notes in Economics and Mathematical Systems Vol. 535, Berlin, 2004.
[3]
M. Basseur. Design of cooperative algorithms for multi-objective optimization: application to the flow-shop scheduling problem. 4OR, 4(3):255--258, 2006.
[4]
J. L. Bentley. Fast algorithms for geometric traveling salesman problems. ORSA Journal on Computing, 4:387--411, 1992.
[5]
M. Ehrgott and X. Gandibleux. Multiple Criteria Optimization: State of the Art Annotated Bibliographic Surveys. Kluwer Academic Publishers, Boston, 2002.
[6]
L. Paquete, M. Chiarandini, and T. Stützle. Pareto Local Optimum Sets in the Biobjective Traveling Salesman Problem: An Experimental Study. In X. Gandibleux, M. Sevaux, K. Sörensen, and V. T'kindt, editors, Metaheuristics for Multiobjective Optimisation, pages 177--199, Berlin, 2004. Springer. Lecture Notes in Economics and Mathematical Systems Vol. 535.
[7]
G. Reinelt. Tsplib - a traveling salesman problem library. ORSA Journal of Computing, 3(4):376--384, 1991.
[8]
E. Ulungu and J. Teghem. The two phases method: An efficient procedure to solve biobjective combinatorial optimization problems. Foundation of Computing and Decision Science, 20:149--156, 1995.

Cited By

View all
  • (2020)Comparison between Single and Multi-Objective Evolutionary Algorithms to Solve the Knapsack Problem and the Travelling Salesman ProblemMathematics10.3390/math81120188:11(2018)Online publication date: 12-Nov-2020
  • (2019)A Two-Phase Multiobjective Local Search for the Device Allocation in the Distributed Integrated Modular AvionicsIEEE Access10.1109/ACCESS.2019.2928059(1-1)Online publication date: 2019
  • (2018)Decision‐making in non‐cooperative games with conflicting self‐objectivesJournal of Multi-Criteria Decision Analysis10.1002/mcda.163925:5-6(130-141)Online publication date: 5-Jul-2018
  • Show More Cited By

Index Terms

  1. Speed-up techniques for solving large-scale bTSP with the Two-Phase Pareto Local Search

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
    July 2008
    1814 pages
    ISBN:9781605581309
    DOI:10.1145/1389095
    • Conference Chair:
    • Conor Ryan,
    • Editor:
    • Maarten Keijzer
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 12 July 2008

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. local search
    2. multiobjective combinatorial optimization
    3. speed-up techniques
    4. tsp

    Qualifiers

    • Poster

    Conference

    GECCO08
    Sponsor:

    Acceptance Rates

    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)5
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 08 Mar 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2020)Comparison between Single and Multi-Objective Evolutionary Algorithms to Solve the Knapsack Problem and the Travelling Salesman ProblemMathematics10.3390/math81120188:11(2018)Online publication date: 12-Nov-2020
    • (2019)A Two-Phase Multiobjective Local Search for the Device Allocation in the Distributed Integrated Modular AvionicsIEEE Access10.1109/ACCESS.2019.2928059(1-1)Online publication date: 2019
    • (2018)Decision‐making in non‐cooperative games with conflicting self‐objectivesJournal of Multi-Criteria Decision Analysis10.1002/mcda.163925:5-6(130-141)Online publication date: 5-Jul-2018
    • (2017)Reference Line Guided Pareto Local Search for Bi-Objective Traveling Salesman Problem22017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC)10.1109/CSE-EUC.2017.20(50-56)Online publication date: Jul-2017
    • (2017)Greedy Based Pareto Local Search for Bi-objective Robust Airport Gate Assignment ProblemSimulated Evolution and Learning10.1007/978-3-319-68759-9_56(694-705)Online publication date: 14-Oct-2017

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media