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On the Empirical Scaling Behaviour of State-of-the-art Local Search Algorithms for the Euclidean TSP

Authors:

Jérémie Dubois-Lacoste,

Holger H. Hoos,

Thomas StützleAuthors Info & Claims

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

Pages 377 - 384

https://doi.org/10.1145/2739480.2754747

Published: 11 July 2015 Publication History

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Abstract

We present a thorough empirical investigation of the scaling behaviour of state-of-the-art local search algorithms for the TSP; in particular, we study the scaling of running time required for finding optimal solutions to Euclidean TSP instances. We use a recently introduced bootstrapping approach to assess the statistical significance of the scaling models thus obtained and contrast these models with those recently reported for the Concorde algorithm. In particular, we answer the question whether the scaling behaviour of state-of-the-art local search algorithms for the TSP differs by more than a constant from that required by Concorde to find the first optimal solution to a given TSP instance.

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Cited By

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Heins JSchäpermeier LKerschke PWhitley D(2024)Dancing to the State of the Art?Parallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70055-2_7(100-115)Online publication date: 14-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70055-2_7
Vinzent Seiler MRook JHeins JLudger Preuß OBossek JTrautmann H(2023)Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP2023 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI52147.2023.10372008(361-368)Online publication date: 5-Dec-2023
https://doi.org/10.1109/SSCI52147.2023.10372008
Heins JBossek JPohl JSeiler MTrautmann HKerschke P(2023)A study on the effects of normalized TSP features for automated algorithm selectionTheoretical Computer Science10.1016/j.tcs.2022.10.019940:PB(123-145)Online publication date: 9-Jan-2023
https://dl.acm.org/doi/10.1016/j.tcs.2022.10.019
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Index Terms

On the Empirical Scaling Behaviour of State-of-the-art Local Search Algorithms for the Euclidean TSP
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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Published In

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

July 2015

1496 pages

ISBN:9781450334723

DOI:10.1145/2739480

Editor:
Sara Silva
Universidade de Lisboa, Portugal
,
General Chair:
Anna I. Esparcia-Alcázar
Universitat Politècnica de València, Spain

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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2015

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F.R.S.-FNRS
Belgian Science Policy Office
NSERC

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GECCO '15

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SIGEVO

GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

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GECCO '15 Paper Acceptance Rate 182 of 505 submissions, 36%;

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

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Heins JSchäpermeier LKerschke PWhitley D(2024)Dancing to the State of the Art?Parallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70055-2_7(100-115)Online publication date: 14-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70055-2_7
Vinzent Seiler MRook JHeins JLudger Preuß OBossek JTrautmann H(2023)Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP2023 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI52147.2023.10372008(361-368)Online publication date: 5-Dec-2023
https://doi.org/10.1109/SSCI52147.2023.10372008
Heins JBossek JPohl JSeiler MTrautmann HKerschke P(2023)A study on the effects of normalized TSP features for automated algorithm selectionTheoretical Computer Science10.1016/j.tcs.2022.10.019940:PB(123-145)Online publication date: 9-Jan-2023
https://dl.acm.org/doi/10.1016/j.tcs.2022.10.019
Heins JBossek JPohl JSeiler MTrautmann HKerschke PFinck SHellwig MOliveto P(2021)On the potential of normalized TSP features for automated algorithm selectionProceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms10.1145/3450218.3477308(1-15)Online publication date: 6-Sep-2021
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Zhao KLiu SYu JRong Y(2021)Towards Feature-free TSP Solver Selection: A Deep Learning Approach2021 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN52387.2021.9533538(1-8)Online publication date: 2021
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Herraiz AGutierrez MOrtega-Mier M(2021)Equivalent cyclic polygon of a euclidean travelling salesman problem tour and modified formulationCentral European Journal of Operations Research10.1007/s10100-021-00784-z30:4(1427-1450)Online publication date: 2-Dec-2021
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Pushak YMu ZHoos H(2020)Empirical scaling analyzerAI Communications10.3233/AIC-20063033:2(93-111)Online publication date: 1-Jan-2020
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Bossek JKerschke PNeumann AWagner MNeumann FTrautmann HFriedrich TDoerr CArnold D(2019)Evolving diverse TSP instances by means of novel and creative mutation operatorsProceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms10.1145/3299904.3340307(58-71)Online publication date: 27-Aug-2019
https://dl.acm.org/doi/10.1145/3299904.3340307
Kerschke PHoos HNeumann FTrautmann H(2018)Automated Algorithm Selection: Survey and PerspectivesEvolutionary Computation10.1162/evco_a_00242(1-47)Online publication date: 26-Nov-2018
https://doi.org/10.1162/evco_a_00242
Kerschke PKotthoff LBossek JHoos HTrautmann H(2018)Leveraging TSP Solver Complementarity through Machine LearningEvolutionary Computation10.1162/evco_a_0021526:4(597-620)Online publication date: Dec-2018
https://doi.org/10.1162/evco_a_00215
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Reducing path TSP to TSP

On the empirical scaling of running time for finding optimal solutions to the TSP

Reducing Path TSP to TSP

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