Skip to main content
SpringerLink
Log in
Book cover

Asia-Pacific Conference on Simulated Evolution and Learning

SEAL 2017: Simulated Evolution and Learning pp 110–121Cite as

Exact Approaches for the Travelling Thief Problem

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10593))

Abstract

Many evolutionary and constructive heuristic approaches have been introduced in order to solve the Travelling Thief Problem (TTP). However, the accuracy of such approaches is unknown due to their inability to find global optima. In this paper, we propose three exact algorithms and a hybrid approach to the TTP. We compare these with state-of-the-art approaches to gather a comprehensive overview on the accuracy of heuristic methods for solving small TTP instances.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    All instances are available online: http://cs.adelaide.edu.au/~optlog/research/ttp.php.

  2. 2.

    They are fitted polynomials of degree six used only for visualisation purposes.

References

  1. Applegate, D., Bixby, R., Chvatal, V., Cook, W.: Concorde TSP solver (2006). http://www.math.uwaterloo.ca/tsp/concorde.html

  2. Benchimol, P., Van Hoeve, W.-J., Régin, J.-C., Rousseau, L.-M., Rueher, M.: Improved filtering for weighted circuit constraints. Constraints 17(3), 205–233 (2012). doi:10.1007/s10601-012-9119-x. ISSN 1572-9354

    Article  MathSciNet  MATH  Google Scholar 

  3. Bonyadi, M., Michalewicz, Z., Barone, L.: The travelling thief problem: the first step in the transition from theoretical problems to realistic problems. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 1037–1044 (2013)

    Google Scholar 

  4. Bonyadi, M.R., Michalewicz, Z., Przybylek, M.R., Wierzbicki, A.: Socially inspired algorithms for the travelling thief problem. In: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, GECCO 2014, pp. 421–428. ACM (2014)

    Google Scholar 

  5. El Yafrani, M., Ahiod, B.: Cosolver2B: an efficient local search heuristic for the travelling thief problem. In: 2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA), pp. 1–5. IEEE (2015)

    Google Scholar 

  6. El Yafrani, M., Ahiod, B.: Population-based vs. single-solution heuristics for the travelling thief problem. In: Proceedings of the Genetic and Evolutionary Computation Conference 2016, GECCO 2016, pp. 317–324. ACM (2016)

    Google Scholar 

  7. Faulkner, H., Polyakovskiy, S., Schultz, T., Wagner, M.: Approximate approaches to the traveling thief problem. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, GECCO 2015, pp. 385–392. ACM (2015)

    Google Scholar 

  8. Held, M., Karp, R.M.: A dynamic programming approach to sequencing problems. In: Proceedings of the 1961 16th ACM National Meeting, ACM 1961, pp. 71.201-71.204. ACM (1961)

    Google Scholar 

  9. Hooker, J.N.: Logic, optimization, and constraint programming. INFORMS J. Comput. 14(4), 295–321 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  10. Lin, S., Kernighan, B.W.: An effective heuristic algorithm for the traveling-salesman problem. Oper. Res. 21(2), 498–516 (1973)

    Article  MathSciNet  MATH  Google Scholar 

  11. Mei, Y., Li, X., Yao, X.: Improving efficiency of heuristics for the large scale traveling thief problem. In: Dick, G., et al. (eds.) SEAL 2014. LNCS, vol. 8886, pp. 631–643. Springer, Cham (2014). doi:10.1007/978-3-319-13563-2_53. ISBN 978-3-319-13563-2

    Google Scholar 

  12. Mei, Y., Li, X., Salim, F., Yao, X.: Heuristic evolution with genetic programming for traveling thief problem. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 2753–2760, May 2015. doi:10.1109/CEC.2015.7257230

  13. Mei, Y., Li, X., Yao, X.: On investigation of interdependence between sub-problems of the travelling thief problem. Soft Comput. 20(1), 157–172 (2016)

    Article  Google Scholar 

  14. Neumann, F., Polyakovskiy, S., Skutella, M., Stougie, L., Wu, J.: A Fully Polynomial Time Approximation Scheme for Packing While Traveling. ArXiv e-prints (2017)

    Google Scholar 

  15. Pisinger, D.: Advanced Generator for 0–1 Knapsack Problem. http://www.diku.dk/~pisinger/codes.html

  16. Pisinger, D.: Where are the hard knapsack problems? Comput. Oper. Res. 32(9), 2271–2284 (2005). doi:10.1016/j.cor.2004.03.002. ISSN 0305-0548

    Article  MathSciNet  MATH  Google Scholar 

  17. Polyakovskiy, S., Neumann, F.: The packing while traveling problem. Eur. J. Oper. Res. 258(2), 424–439 (2017)

    Article  MathSciNet  Google Scholar 

  18. Polyakovskiy, S., Bonyadi, M.R., Wagner, M., Michalewicz, Z., Neumann, F.: A comprehensive benchmark set and heuristics for the traveling thief problem. In: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, GECCO 2014, pp. 477–484. ACM (2014)

    Google Scholar 

  19. Refalo, P.: Impact-based search strategies for constraint programming. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 557–571. Springer, Heidelberg (2004). doi:10.1007/978-3-540-30201-8_41

    Chapter  Google Scholar 

  20. Reinelt, G.: TSPLIB—a traveling salesman problem library. ORSA J. Comput. 3(4), 376–384 (1991)

    Article  MATH  Google Scholar 

  21. Stützle, T., Hoos, H.H.: MAX MIN ant system. Future Gener. Comput. Syst. 16(8), 889–914 (2000)

    Article  MATH  Google Scholar 

  22. Wagner, M.: Stealing items more efficiently with ants: a swarm intelligence approach to the travelling thief problem. In: Dorigo, M., Birattari, M., Li, X., López-Ibáñez, M., Ohkura, K., Pinciroli, C., Stützle, T. (eds.) ANTS 2016. LNCS, vol. 9882, pp. 273–281. Springer, Cham (2016). doi:10.1007/978-3-319-44427-7_25

    Chapter  Google Scholar 

  23. Wagner, M., Lindauer, M., Mısır, M., Nallaperuma, S., Hutter, F.: A case study of algorithm selection for the traveling thief problem. J. Heuristics pp. 1–26 (2017)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Australian Research councils through grants DP130104395 and DE160100850, and by the supercomputing resources provided by the Phoenix HPC service at the University of Adelaide.

Author information

Authors and Affiliations

  1. Optimisation and Logistics, School of Computer Science, The University of Adelaide, Adelaide, SA, Australia

    Junhua Wu, Markus Wagner, Sergey Polyakovskiy & Frank Neumann

Authors
  1. Junhua Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Markus Wagner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sergey Polyakovskiy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Frank Neumann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sergey Polyakovskiy .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  2. City University of Hong Kong, Hong Kong, Kowloon, Hong Kong

    Kay Chen Tan

  3. Victoria University of Wellington, Wellington, Wellington, New Zealand

    Mengjie Zhang

  4. Southern University of Science and Technology, Shenzhen, China

    Ke Tang

  5. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

  6. City University of Hong Kong, Kowloon Tong, Hong Kong

    Qingfu Zhang

  7. Peking University, Beijing, China

    Ying Tan

  8. University of Leipzig, Leipzig, Germany

    Martin Middendorf

  9. University of Surrey, Guildford, Surrey, United Kingdom

    Yaochu Jin

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Wu, J., Wagner, M., Polyakovskiy, S., Neumann, F. (2017). Exact Approaches for the Travelling Thief Problem. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-68759-9_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68758-2

  • Online ISBN: 978-3-319-68759-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation