IDEAS home Printed from https://ideas.repec.org/a/spr/jglopt/v62y2015i1p131-154.html
   My bibliography  Save this article

An information guided framework for simulated annealing

Author

Listed:
  • Chao Yang
  • Mrinal Kumar

Abstract

This paper presents an information guided framework for stochastic optimization with simulated annealing. Information gathered during randomized exploration of the search domain is used as feedback with progressively increasing gain to drive the optimization procedure, potentially causing the annealing temperature to rise during the algorithm’s execution. The benefits of reheating during the annealing process are shown in terms of significant improvement in the algorithm’s performance, while also staying within bounds for its convergence. A guided-annealing temperature is defined that incorporates information into the annealing schedule. The resulting algorithm has two phases: phase I performs nearly unrestricted exploration as a reconnaissance of the optimization domain and phase II “re-heats” the annealing procedure and exploits information gathered during phase I. Phase I flows seamlessly into phase II via an information effectiveness parameter without need for user input. Conditions are derived to prevent excessive reheating that may jeopardize convergence characteristics. Several examples are presented to test the new algorithm. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Chao Yang & Mrinal Kumar, 2015. "An information guided framework for simulated annealing," Journal of Global Optimization, Springer, vol. 62(1), pages 131-154, May.
    • Handle: RePEc:spr:jglopt:v:62:y:2015:i:1:p:131-154
    DOI: 10.1007/s10898-014-0229-4
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10898-014-0229-4
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10898-014-0229-4?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. John Rust, 1997. "Using Randomization to Break the Curse of Dimensionality," Econometrica, Econometric Society, vol. 65(3), pages 487-516, May.
    2. Bruce Hajek, 1988. "Cooling Schedules for Optimal Annealing," Mathematics of Operations Research, INFORMS, vol. 13(2), pages 311-329, May.
    3. Richard Bellman, 1957. "On a Dynamic Programming Approach to the Caterer Problem--I," Management Science, INFORMS, vol. 3(3), pages 270-278, April.
    Full references (including those not matched with items on IDEAS)

    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. Mercedes Esteban-Bravo & Jose M. Vidal-Sanz & Gökhan Yildirim, 2014. "Valuing Customer Portfolios with Endogenous Mass and Direct Marketing Interventions Using a Stochastic Dynamic Programming Decomposition," Marketing Science, INFORMS, vol. 33(5), pages 621-640, September.
    2. Bond, Craig A. & Iverson, Terrence, 2011. "Modeling Information in Environmental Decision-Making," Western Economics Forum, Western Agricultural Economics Association, vol. 10(2), pages 1-17.
    3. Hicks, Robert L. & Schnier, Kurt E., 2006. "A Spatial Model of Dolphin Avoidance in the Eastern Tropical Pacific Ocean," 2006 Annual meeting, July 23-26, Long Beach, CA 21290, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    4. Yongyang Cai & Kenneth Judd, 2015. "Dynamic programming with Hermite approximation," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 81(3), pages 245-267, June.
    5. Arellano, Cristina & Maliar, Lilia & Maliar, Serguei & Tsyrennikov, Viktor, 2016. "Envelope condition method with an application to default risk models," Journal of Economic Dynamics and Control, Elsevier, vol. 69(C), pages 436-459.
    6. Emanuele Barca & Giuseppe Passarella & Michele Vurro & Alberto Morea, 2015. "MSANOS: Data-Driven, Multi-Approach Software for Optimal Redesign of Environmental Monitoring Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(2), pages 619-644, January.
    7. Yongyang Cai & Kenneth Judd & Greg Thain & Stephen Wright, 2015. "Solving Dynamic Programming Problems on a Computational Grid," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 261-284, February.
    8. Cervellera, Cristiano & Chen, Victoria C.P. & Wen, Aihong, 2006. "Optimization of a large-scale water reservoir network by stochastic dynamic programming with efficient state space discretization," European Journal of Operational Research, Elsevier, vol. 171(3), pages 1139-1151, June.
    9. De Pinto, Alessandro & Nelson, Gerald C., 2004. "A Dynamic Model Of Land Use Change With Spatially Explicit Data," 2004 Annual meeting, August 1-4, Denver, CO 20314, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    10. Norets, Andriy & Shimizu, Kenichi, 2024. "Semiparametric Bayesian estimation of dynamic discrete choice models," Journal of Econometrics, Elsevier, vol. 238(2).
    11. Pierre Bernhard & Marc Deschamps, 2017. "Kalman on dynamics and contro, Linear System Theory, Optimal Control, and Filter," Working Papers 2017-10, CRESE.
    12. Jones, Randall E. & Cacho, Oscar J., 2000. "A Dynamic Optimisation Model of Weed Control," 2000 Conference (44th), January 23-25, 2000, Sydney, Australia 123685, Australian Agricultural and Resource Economics Society.
    13. Voelkel, Michael A. & Sachs, Anna-Lena & Thonemann, Ulrich W., 2020. "An aggregation-based approximate dynamic programming approach for the periodic review model with random yield," European Journal of Operational Research, Elsevier, vol. 281(2), pages 286-298.
    14. Pam Norton & Ravi Phatarfod, 2008. "Optimal Strategies In One-Day Cricket," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 25(04), pages 495-511.
    15. Nikolaj Malchow-Møller & Michael Svarer, 2003. "Estimation of the multinomial logit model with random effects," Applied Economics Letters, Taylor & Francis Journals, vol. 10(7), pages 389-392.
    16. Alexei Onatski & Noah Williams, 2003. "Modeling Model Uncertainty," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1087-1122, September.
    17. Aghayi, Nazila & Maleki, Bentolhoda, 2016. "Efficiency measurement of DMUs with undesirable outputs under uncertainty based on the directional distance function: Application on bank industry," Energy, Elsevier, vol. 112(C), pages 376-387.
    18. C. P. Stephens & W. Baritompa, 1998. "Global Optimization Requires Global Information," Journal of Optimization Theory and Applications, Springer, vol. 96(3), pages 575-588, March.
    19. Tan, Madeleine Sui-Lay, 2016. "Policy coordination among the ASEAN-5: A global VAR analysis," Journal of Asian Economics, Elsevier, vol. 44(C), pages 20-40.
    20. D. W. K. Yeung, 2008. "Dynamically Consistent Solution For A Pollution Management Game In Collaborative Abatement With Uncertain Future Payoffs," International Game Theory Review (IGTR), World Scientific Publishing Co. Pte. Ltd., vol. 10(04), pages 517-538.

    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:jglopt:v:62:y:2015:i:1:p:131-154. 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.