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

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Computer Science – CACIC 2021 (CACIC 2021)

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

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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.

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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.

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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
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  2. Hugo Alfonso
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  3. Gabriela Minetti
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  4. Carolina Salto
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Corresponding author

Correspondence to Gabriela Minetti .

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Editors and Affiliations

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

    Patricia Pesado

  2. National University of Salta, Salta, Argentina

    Gustavo Gil

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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

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05902-5

  • Online ISBN: 978-3-031-05903-2

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