IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v28y2013i5p2049-2065.html
   My bibliography  Save this article

Proposal adaptation in simulated annealing for continuous optimization problems

Author

Listed:
  • Antti Solonen

Abstract

In recent years, adaptive Markov Chain Monte Carlo (MCMC) methods have become a standard tool for Bayesian parameter estimation. In adaptive MCMC, the past iterations are used to tune the proposal distribution of the algorithm. The same adaptation mechanisms can be used in Simulated Annealing (SA), a popular optimization method based on MCMC. The difficulty in using adaptation directly in SA is that the target function changes along the iterations in the annealing process, and the adaptation should keep up with the annealing. In this paper, a mechanism for automatically tuning the proposal distribution in SA is proposed. The approach is based on the Adaptive Metropolis algorithm of Haario et al. (Bernoulli 7(2):223–242, 2001 ), combined with a weighting mechanism to account for the cooling target. The proposed adaptation mechanism does not add any computational complexity to the problem in terms of objective function evaluations. The effect of adaptation is demonstrated using two benchmark problems, showing that the proposed adaptation mechanism can significantly improve optimization results compared to non-adaptive SA. The approach is presented for continuous optimization problems and generalization to integer and mixed-integer problems is a topic of future research. Copyright Springer-Verlag Berlin Heidelberg 2013

Suggested Citation

  • Antti Solonen, 2013. "Proposal adaptation in simulated annealing for continuous optimization problems," Computational Statistics, Springer, vol. 28(5), pages 2049-2065, October.
    • Handle: RePEc:spr:compst:v:28:y:2013:i:5:p:2049-2065
    DOI: 10.1007/s00180-013-0395-8
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00180-013-0395-8
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00180-013-0395-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. R. L. Yang, 2000. "Convergence of the Simulated Annealing Algorithm for Continuous Global Optimization," Journal of Optimization Theory and Applications, Springer, vol. 104(3), pages 691-716, March.
    2. L. Ingber, 1996. "Adaptive simulated annealing (ASA): Lessons learned," Lester Ingber Papers 96as, Lester Ingber.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Giuseppe Montesi & Giovanni Papiro & Massimiliano Fazzini & Alessandro Ronga, 2020. "Stochastic Optimization System for Bank Reverse Stress Testing," JRFM, MDPI, vol. 13(8), pages 1-44, August.
    2. Wen-Li Dong & Xing Li & Zheng Peng, 2019. "A Simulated Annealing-Based Barzilai–Borwein Gradient Method for Unconstrained Optimization Problems," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(04), pages 1-12, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gerber, Mathieu & Bornn, Luke, 2018. "Convergence results for a class of time-varying simulated annealing algorithms," Stochastic Processes and their Applications, Elsevier, vol. 128(4), pages 1073-1094.
    2. Nasir Aminu, 2018. "Evaluation of a DSGE Model of Energy in the United Kingdom Using Stationary Data," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 1033-1068, April.
    3. Fan, Jingwen & Minford, Patrick & Ou, Zhirong, 2016. "The role of fiscal policy in Britain's Great Inflation," Economic Modelling, Elsevier, vol. 58(C), pages 203-218.
    4. Ingber, Lester, 2000. "High-resolution path-integral development of financial options," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 283(3), pages 529-558.
    5. Graeme J. Doole & David J. Pannell, 2008. "Optimisation of a Large, Constrained Simulation Model using Compressed Annealing," Journal of Agricultural Economics, Wiley Blackwell, vol. 59(1), pages 188-206, February.
    6. Lehmann, Sebastian & Huth, Andreas, 2015. "Fast calibration of a dynamic vegetation model with minimum observation data," Ecological Modelling, Elsevier, vol. 301(C), pages 98-105.
    7. Liu, Chunping & Minford, Patrick, 2014. "Comparing behavioural and rational expectations for the US post-war economy," Economic Modelling, Elsevier, vol. 43(C), pages 407-415.
    8. Vo Le & Kent Matthews & David Meenagh & Patrick Minford & Zhiguo Xiao, 2014. "Banking and the Macroeconomy in China: A Banking Crisis Deferred?," Open Economies Review, Springer, vol. 25(1), pages 123-161, February.
    9. Liu, Chunping & Minford, Patrick, 2014. "How important is the credit channel? An empirical study of the US banking crisis," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 119-134.
    10. Li Dai & Patrick Minford & Peng Zhou, 2015. "A DSGE model of China," Applied Economics, Taylor & Francis Journals, vol. 47(59), pages 6438-6460, December.
    11. Aminu, Nasir & Meenagh, David & Minford, Patrick, 2018. "The role of energy prices in the Great Recession — A two-sector model with unfiltered data," Energy Economics, Elsevier, vol. 71(C), pages 14-34.
    12. Efrat Taig & Ohad Ben-Shahar, 2019. "Gradient Surfing: A New Deterministic Approach for Low-Dimensional Global Optimization," Journal of Optimization Theory and Applications, Springer, vol. 180(3), pages 855-878, March.
    13. Le, Vo Phuong Mai & Meenagh, David & Minford, Patrick, 2016. "Monetarism rides again? US monetary policy in a world of Quantitative Easing," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 44(C), pages 85-102.
    14. Marc Robini & Pierre-Jean Reissman, 2013. "From simulated annealing to stochastic continuation: a new trend in combinatorial optimization," Journal of Global Optimization, Springer, vol. 56(1), pages 185-215, May.
    15. M. Bowman & L. Ingber, 1997. "Canonical momenta of nonlinear combat," Lester Ingber Papers 97cm, Lester Ingber.
    16. Supriya Dhabal & Palaniandavar Venkateswaran, 2014. "Two-Dimensional IIR Filter Design Using Simulated Annealing Based Particle Swarm Optimization," Journal of Optimization, Hindawi, vol. 2014, pages 1-10, September.
    17. Lester Ingber & Radu Paul Mondescu, 2000. "Optimization of Trading Physics Models of Markets," Papers physics/0007075, arXiv.org.
    18. Gianfranco Gagliardi & Vincenzo Gallelli & Antonio Violi & Marco Lupia & Gianni Cario, 2024. "Optimal Placement of Sensors in Traffic Networks Using Global Search Optimization Techniques Oriented towards Traffic Flow Estimation and Pollutant Emission Evaluation," Sustainability, MDPI, vol. 16(9), pages 1-23, April.
    19. Olayinka Oyekola, 2022. "How Resilient Is the U.S. Economy to Foreign Disturbances?," Mathematics, MDPI, vol. 10(9), pages 1-33, April.
    20. Patrick Minford & Zhirong Ou & Michael Wickens, 2015. "Revisiting the Great Moderation: Policy or Luck?," Open Economies Review, Springer, vol. 26(2), pages 197-223, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:28:y:2013:i:5:p:2049-2065. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.