IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v81y2011i8p915-920.html
   My bibliography  Save this article

On simulated annealing with temperature-dependent energy and temperature-dependent communication

Author

Listed:
  • Robini, Marc C.
  • Reissman, Pierre-Jean

Abstract

Simulated annealing (SA) is a generic optimization method that is quite popular because of its ease of implementation and its optimal convergence properties. Still, SA is widely reported to converge very slowly and it is common practice to allow extra freedom in its design at the expense of losing global convergence guarantees. In this paper, we derive simple sufficient conditions for the global convergence of SA when the cost function and the candidate solution generation mechanism are temperature-dependent. These conditions are surprisingly weak-they do not involve the variations of the cost function with temperature-and exponential cooling makes it possible to be arbitrarily close to the best possible convergence exponent of standard SA.

Suggested Citation

  • Robini, Marc C. & Reissman, Pierre-Jean, 2011. "On simulated annealing with temperature-dependent energy and temperature-dependent communication," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 915-920, August.
    • Handle: RePEc:eee:stapro:v:81:y:2011:i:8:p:915-920
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715211001349
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Bruce Hajek, 1988. "Cooling Schedules for Optimal Annealing," Mathematics of Operations Research, INFORMS, vol. 13(2), pages 311-329, May.
    2. Löwe, Matthias, 1996. "Simulated annealing with time-dependent energy function via Sobolev inequalities," Stochastic Processes and their Applications, Elsevier, vol. 63(2), pages 221-233, November.
    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. Stoica, R.S. & Gregori, P. & Mateu, J., 2005. "Simulated annealing and object point processes: Tools for analysis of spatial patterns," Stochastic Processes and their Applications, Elsevier, vol. 115(11), pages 1860-1882, November.
    2. Pirlot, Marc, 1996. "General local search methods," European Journal of Operational Research, Elsevier, vol. 92(3), pages 493-511, August.
    3. Löwe, Matthias, 1997. "On the invariant measure of non-reversible simulated annealing," Statistics & Probability Letters, Elsevier, vol. 36(2), pages 189-193, December.
    4. Miclo, Laurent, 1995. "Remarques sur l'ergodicité des algorithmes de recuit simulé sur un graphe," Stochastic Processes and their Applications, Elsevier, vol. 58(2), pages 329-360, August.
    5. Yiyo Kuo, 2014. "Design method using hybrid of line-type and circular-type routes for transit network system optimization," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(2), pages 600-613, July.
    6. Rodriguez-Tello, Eduardo & Hao, Jin-Kao & Torres-Jimenez, Jose, 2008. "An improved simulated annealing algorithm for bandwidth minimization," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1319-1335, March.
    7. Sam Hui & Eric Bradlow, 2012. "Bayesian multi-resolution spatial analysis with applications to marketing," Quantitative Marketing and Economics (QME), Springer, vol. 10(4), pages 419-452, December.
    8. Gabriel M. Portal & Marcus Ritt & Leonardo M. Borba & Luciana S. Buriol, 2016. "Simulated annealing for the machine reassignment problem," Annals of Operations Research, Springer, vol. 242(1), pages 93-114, July.
    9. 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.
    10. Triki, E. & Collette, Y. & Siarry, P., 2005. "A theoretical study on the behavior of simulated annealing leading to a new cooling schedule," European Journal of Operational Research, Elsevier, vol. 166(1), pages 77-92, October.
    11. Adam Tauman Kalai & Santosh Vempala, 2006. "Simulated Annealing for Convex Optimization," Mathematics of Operations Research, INFORMS, vol. 31(2), pages 253-266, May.
    12. Pablo Adasme & Ali Dehghan Firoozabadi, 2019. "Facility Location with Tree Topology and Radial Distance Constraints," Complexity, Hindawi, vol. 2019, pages 1-29, November.
    13. Christoph Flamm & Ivo L. Hofacker & Peter F. Stadler & Michael T. Wolfinger, 2001. "Barrier Trees of Degenerate Landscapes," Working Papers 01-09-053, Santa Fe Institute.
    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. Kapetanios, George, 2006. "Cluster analysis of panel data sets using non-standard optimisation of information criteria," Journal of Economic Dynamics and Control, Elsevier, vol. 30(8), pages 1389-1408, August.
    16. Alrefaei, Mahmoud H. & Alawneh, Ameen J., 2005. "Solution quality of random search methods for discrete stochastic optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 68(2), pages 115-125.
    17. Taillard, Eric D. & Gambardella, Luca M. & Gendreau, Michel & Potvin, Jean-Yves, 2001. "Adaptive memory programming: A unified view of metaheuristics," European Journal of Operational Research, Elsevier, vol. 135(1), pages 1-16, November.
    18. Woodruff, David L., 1998. "Proposals for chunking and tabu search," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 585-598, April.
    19. Lisic Jonathan & Sang Hejian & Zimmer Stephanie & Zhu Zhengyuan, 2018. "Optimal Stratification and Allocation for the June Agricultural Survey," Journal of Official Statistics, Sciendo, vol. 34(1), pages 121-148, March.
    20. Mario Bravo & Mathieu Faure, 2013. "Reinforcement Learning with Restrictions on the Action Set," AMSE Working Papers 1335, Aix-Marseille School of Economics, France, revised 01 Jul 2013.

    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:eee:stapro:v:81:y:2011:i:8:p:915-920. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.