Skip to main content
Springer Nature Link
Log in

Local Search Based on a Local Utopia Point for the Multiobjective Travelling Salesman Problem

  • Conference paper
  • First Online:
Intelligent Data Engineering and Automated Learning – IDEAL 2015 (IDEAL 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9375))

  • 1519 Accesses

Abstract

Performing a local search around solutions found by an evolutionary algorithm is a common practice. Local search is well known to significantly improve the solutions, in particular in the case of combinatorial problems. In this paper a new local search procedure is proposed that uses a locally established utopia point. In the tests in which several instances of the Travelling Salesman Problem (TSP) were solved using an evolutionary algorithm the proposed local search procedure outperformed a local search procedure based on Pareto dominance. Because the local search is focused on improving individual solutions and the multiobjective evolutionary algorithm can improve diversity, various strategies of sharing computational resources between the evolutionary algorithm and the local search are used in this paper. The results attained by the tested methods are compared with respect to computation time, which allows a fair comparison between strategies that distribute computational resources between the evolutionary optimization and the local search in various proportions.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Chiang, C.W., Lee, W.P., Heh, J.S.: A 2-opt based differential evolution for global optimization. Appl. Soft Comput. 10(4), 1200–1207 (2010)

    Article  Google Scholar 

  2. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002)

    Article  Google Scholar 

  3. Derrac, J., Garca, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1(1), 3–18 (2011)

    Article  Google Scholar 

  4. Dubois-Lacoste, J., et al.: A hybrid TP+PLS algorithm for bi-objective flow-shop scheduling problems. Comput. Oper. Res. 38(8), 1219–1236 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  5. Goldberg, D.E., Voessner, S.: Optimizing global-local search hybrids. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, vol. 1, pp. 220–228. Morgan Kaufmann, Orlando (1999)

    Google Scholar 

  6. Ishibuchi, H.: Memetic algorithms for evolutionary multiobjective combinatorial optimization. In: 2010 40th International Conference on Computers and Industrial Engineering (CIE), pp. 1–2 (2010)

    Google Scholar 

  7. Li, M., Zheng, J.: Spread assessment for evolutionary multi-objective optimization. In: Ehrgott, M., Fonseca, C.M., Gandibleux, X., Hao, J.-K., Sevaux, M. (eds.) EMO 2009. LNCS, vol. 5467, pp. 216–230. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  8. Lust, T., Teghem, J.: Two-phase pareto local search for the biobjective traveling salesman problem. J. Heuristics 16(3), 475–510 (2010)

    Article  MATH  Google Scholar 

  9. Ott, L., Longnecker, M.: An Introduction to Statistical Methods and Data Analysis. Brooks/Cole Cengage Learning, Boston (2010)

    Google Scholar 

  10. Sinha, A., Goldberg, D.E.: Verification and extension of the theory of global-local hybrids. In: Proceedings of GECCO (2001)

    Google Scholar 

  11. Tao, G., Michalewicz, Z.: Inver-over operator for the TSP. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 803–812. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  12. Thibaut Lust: Multiobjective TSP (2015). https://sites.google.com/site/thibautlust/research/multiobjective-tsp. Accessed 29 January 2015

  13. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., da Fonseca, V.G.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7, 117–132 (2002)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Technologies, Institute of Business Informatics, Wroclaw University of Economics, Wroclaw, Poland

    Krzysztof Michalak

Authors
  1. Krzysztof Michalak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Krzysztof Michalak .

Editor information

Editors and Affiliations

  1. Wroclaw University of Technology, Wroclaw, Poland

    Konrad Jackowski

  2. Department of Systems, Wroclaw University of Technology, Wroclaw, Poland

    Robert Burduk

  3. Wroclaw Univ of Tech, Wroclaw, Poland

    Krzysztof Walkowiak

  4. Wroclaw University of Technology, Faculty of Electronics, Wroclaw, Poland

    Michal Wozniak

  5. School of Electrical & Electronic E, University of Manchester, Manchester, United Kingdom

    Hujun Yin

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Michalak, K. (2015). Local Search Based on a Local Utopia Point for the Multiobjective Travelling Salesman Problem. In: Jackowski, K., Burduk, R., Walkowiak, K., Wozniak, M., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2015. IDEAL 2015. Lecture Notes in Computer Science(), vol 9375. Springer, Cham. https://doi.org/10.1007/978-3-319-24834-9_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-24834-9_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24833-2

  • Online ISBN: 978-3-319-24834-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics