Skip to main content
Springer Nature Link
Log in

Internal Behavior Analysis of SA Using Adaptive Strategies to Set the Markov Chain Length

  • Conference paper
  • First Online:
Computer Science – CACIC 2021 (CACIC 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1584))

Included in the following conference series:

  • 321 Accesses

Abstract

In the context of Simulated Annealing (SA) algorithms, a Markov chain transition corresponds to a move in the solution space. The number of transitions or the Markov chain (MC) length at each temperature step is usually constant and empirically set. However, adaptive methods to compute the MC length can be used. This work focus on the effect of using different strategies to set the MC length in the SA behavior. To carry out this analysis, the Water Distribution Network Design (WDND) problem is selected, since it is a multimodal and NP-hard problem interesting to optimize. The results indicate that the use of adaptive strategies to set the MC length improves the solution quality versus the static one. Moreover, the proposed SA achieves the scalability property when the WDND solution space size is considered.

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. Garey, M.R., Johnson, D.S.: Computers and Intractability; A Guide to the Theory of NP-Completeness. W. H. Freeman & Co., USA (1990)

    Google Scholar 

  2. Kirkpatrick, S., Gelatt, C.D., Jr., Vecchi, M.: Optimization by simulated annealing. Science 220, 671–680 (1983)

    Article  MathSciNet  Google Scholar 

  3. Černý, V.: Thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm. J. Optim. Theory Appl. 45, 41–51 (1985)

    Article  MathSciNet  Google Scholar 

  4. Borysenko, O., Byshkin, M.: Coolmomentum: a method for stochastic optimization by langevin dynamics with simulated annealing. Sci. Rep. 11, 10705 (2021)

    Article  Google Scholar 

  5. Lin, S.-W., Cheng, C.-Y., Pourhejazy, P., Ying, K.-C., Lee, C.-H.: New benchmark algorithm for hybrid flowshop scheduling with identical machines. Expert Syst. Appl. 183, 115422 (2021)

    Article  Google Scholar 

  6. Metropolis, N., Rosenbluth, A.W.R.M.N., Teller, A.H.: Nonequilibrium simulated annealing: a faster approach to combinatorial minimization. J. Chem. Phys. 21, 1087–1092 (1953)

    Article  Google Scholar 

  7. Alba, E., Blum, C., Isasi, P., León, C., Gómez, J.A. (eds.), Frontmatter. Wiley, Hoboken (2009). https://doi.org/10.1002/9780470411353

  8. Cardoso, M., Salcedo, R., de Azevedo, S.: Nonequilibrium simulated annealing: a faster approach to combinatorial minimization. Ind. Eng. Chem. Res. 33, 1908–1918 (1994)

    Article  Google Scholar 

  9. Ali, M., Törn, A., Viitanen, S.: A direct search variant of the simulated annealing algorithm for optimization involving continuous variables. Comput. Oper. Res. 29(1), 87–102 (2002)

    Article  MathSciNet  Google Scholar 

  10. Cunha, M., Sousa, J.: Water distribution network design optimization: simulated annealing approach. J. Water Resour. Plan. Manage. 125, 215–221 (1999)

    Article  Google Scholar 

  11. Alfonso, H., Bermudez, C., Minetti, G., Salto, C.: A real case of multi-period water distribution network design solved by a hybrid SA. In: XXVI Congreso Argentino de Ciencias de la Computación (CACIC), pp. 21–3 (2020). http://sedici.unlp.edu.ar/handle/10915/113258

  12. De Corte, A., Sörensen, K.: Hydrogen. http://antor.uantwerpen.be/hydrogen. Accessed 27 Jun 2018

  13. Bermudez, C., Alfonso, H., Minetti, G., Salto, C.: Hybrid simulated annealing to optimize the water distribution network design: a real case. In: Pesado, P., Eterovic, J. (eds.) CACIC 2020. CCIS, vol. 1409, pp. 19–34. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75836-3_2

    Chapter  Google Scholar 

  14. Bermudez, C., Alfonso, H., Minetti, G.F., Salto, C.: Performance analysis of simulated annealing using adaptive markov chain length. In: XXVII Congreso Argentino de Ciencias de la Computación (CACIC 2021), pp. 21–30 (2021)

    Google Scholar 

  15. Bermudez, C., Minetti, G., Salto, C.: SA to optimize the multi-period water distribution network design. In: XXIX Congreso Argentino de Ciencias de la Computación (CACIC 2018), pp. 12–21 (2018)

    Google Scholar 

  16. Bermudez, C., Salto, C., Minetti, G.: Designing a multi-period water distribution network with a hybrid simulated annealing. In: XLVIII JAIIO: XX Simposio Argentino de Inteligencia Artificial (ASAI 2019), pp. 39–52 (2019)

    Google Scholar 

  17. Rossman, L.: The EPANET Programmer’s Toolkit for Analysis of Water Distribution Systems (1999)

    Google Scholar 

  18. De Corte, A., Sörensen, K.: An iterated local search algorithm for water distribution network design optimization. Network 67(3), 187–198 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the support of Universidad Nacional de La Pampa (Project FI-CD-107/20) and the Incentive Program from MINCyT, Argentina. The last two authors are grateful for the support of the HUMAN-CENTERED SMART MOBILITY (HUMOVE) project, PID2020-116727RB-I00, Spain. The last author is also funded by CONICET, Argentina.

Author information

Authors and Affiliations

  1. Facultad de Ingeniería, Universidad Nacional de La Pampa, General Pico, Argentina

    Carlos Bermudez, Hugo Alfonso, Gabriela Minetti & Carolina Salto

  2. CONICET, General Pico, Argentina

    Carolina Salto

Authors
  1. Carlos Bermudez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hugo Alfonso
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gabriela Minetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carolina Salto
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gabriela Minetti .

Editor information

Editors and Affiliations

  1. National University of La Plata, La Plata, Argentina

    Patricia Pesado

  2. National University of Salta, Salta, Argentina

    Gustavo Gil

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bermudez, C., Alfonso, H., Minetti, G., Salto, C. (2022). Internal Behavior Analysis of SA Using Adaptive Strategies to Set the Markov Chain Length. In: Pesado, P., Gil, G. (eds) Computer Science – CACIC 2021. CACIC 2021. Communications in Computer and Information Science, vol 1584. Springer, Cham. https://doi.org/10.1007/978-3-031-05903-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-05903-2_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05902-5

  • Online ISBN: 978-3-031-05903-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics