IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v174y2006i3p1802-1815.html
   My bibliography  Save this article

A modified Hooke and Jeeves algorithm with likelihood ratio performance extrapolation for simulation optimization

Author

Listed:
  • Alkhamis, Talal M.
  • Ahmed, Mohamed A.

Abstract

No abstract is available for this item.

Suggested Citation

  • Alkhamis, Talal M. & Ahmed, Mohamed A., 2006. "A modified Hooke and Jeeves algorithm with likelihood ratio performance extrapolation for simulation optimization," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1802-1815, November.
    • Handle: RePEc:eee:ejores:v:174:y:2006:i:3:p:1802-1815
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(05)00429-7
    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. Mahmoud H. Alrefaei & Sigrún Andradóttir, 1999. "A Simulated Annealing Algorithm with Constant Temperature for Discrete Stochastic Optimization," Management Science, INFORMS, vol. 45(5), pages 748-764, May.
    2. David G. Humphrey & James R. Wilson, 2000. "A Revised Simplex Search Procedure for Stochastic Simulation Response Surface Optimization," INFORMS Journal on Computing, INFORMS, vol. 12(4), pages 272-283, November.
    3. Russell R. Barton & John S. Ivey, Jr., 1996. "Nelder-Mead Simplex Modifications for Simulation Optimization," Management Science, INFORMS, vol. 42(7), pages 954-973, July.
    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. M Laguna & J Molina & F Pérez & R Caballero & A G Hernández-Díaz, 2010. "The challenge of optimizing expensive black boxes: a scatter search/rough set theory approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 53-67, January.

    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. Satyajith Amaran & Nikolaos V. Sahinidis & Bikram Sharda & Scott J. Bury, 2016. "Simulation optimization: a review of algorithms and applications," Annals of Operations Research, Springer, vol. 240(1), pages 351-380, May.
    2. Chang, Kuo-Hao, 2012. "Stochastic Nelder–Mead simplex method – A new globally convergent direct search method for simulation optimization," European Journal of Operational Research, Elsevier, vol. 220(3), pages 684-694.
    3. M Laguna & J Molina & F Pérez & R Caballero & A G Hernández-Díaz, 2010. "The challenge of optimizing expensive black boxes: a scatter search/rough set theory approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 53-67, January.
    4. Rosen, Scott L. & Harmonosky, Catherine M. & Traband, Mark T., 2007. "A simulation optimization method that considers uncertainty and multiple performance measures," European Journal of Operational Research, Elsevier, vol. 181(1), pages 315-330, August.
    5. Weihong Cai & Fengxi Duan, 2023. "Task Scheduling for Federated Learning in Edge Cloud Computing Environments by Using Adaptive-Greedy Dingo Optimization Algorithm and Binary Salp Swarm Algorithm," Future Internet, MDPI, vol. 15(11), pages 1-23, October.
    6. W. Liu & Y. H. Dai, 2001. "Minimization Algorithms Based on Supervisor and Searcher Cooperation," Journal of Optimization Theory and Applications, Springer, vol. 111(2), pages 359-379, November.
    7. Shamsuddin Ahmed, 2013. "Performance of derivative free search ANN training algorithm with time series and classification problems," Computational Statistics, Springer, vol. 28(5), pages 1881-1914, October.
    8. Wang, Honggang, 2012. "Retrospective optimization of mixed-integer stochastic systems using dynamic simplex linear interpolation," European Journal of Operational Research, Elsevier, vol. 217(1), pages 141-148.
    9. Sigrún Andradóttir & Andrei A. Prudius, 2009. "Balanced Explorative and Exploitative Search with Estimation for Simulation Optimization," INFORMS Journal on Computing, INFORMS, vol. 21(2), pages 193-208, May.
    10. Zhang, Jingrui & Li, Zhuoyun & Wang, Beibei, 2021. "Within-day rolling optimal scheduling problem for active distribution networks by multi-objective evolutionary algorithm based on decomposition integrating with thought of simulated annealing," Energy, Elsevier, vol. 223(C).
    11. Katsumi Morikawa & Katsuhiko Takahashi & Daisuke Hirotani, 2018. "Performance evaluation of candidate appointment schedules using clearing functions," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 509-518, March.
    12. L. Jeff Hong & Barry L. Nelson, 2006. "Discrete Optimization via Simulation Using COMPASS," Operations Research, INFORMS, vol. 54(1), pages 115-129, February.
    13. Gülcü, Ayla & Akkan, Can, 2020. "Robust university course timetabling problem subject to single and multiple disruptions," European Journal of Operational Research, Elsevier, vol. 283(2), pages 630-646.
    14. Sudarshan Kumar & Tiziana Di Matteo & Anindya S. Chakrabarti, 2020. "Disentangling shock diffusion on complex networks: Identification through graph planarity," Papers 2001.01518, arXiv.org.
    15. 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.
    16. Jie Xu & Barry L. Nelson & L. Jeff Hong, 2013. "An Adaptive Hyperbox Algorithm for High-Dimensional Discrete Optimization via Simulation Problems," INFORMS Journal on Computing, INFORMS, vol. 25(1), pages 133-146, February.
    17. Lysa Porth & Milton Boyd & Jeffrey Pai, 2016. "Reducing Risk Through Pooling and Selective Reinsurance Using Simulated Annealing: An Example from Crop Insurance," The Geneva Papers on Risk and Insurance Theory, Springer;International Association for the Study of Insurance Economics (The Geneva Association), vol. 41(2), pages 163-191, September.
    18. David G. Humphrey & James R. Wilson, 2000. "A Revised Simplex Search Procedure for Stochastic Simulation Response Surface Optimization," INFORMS Journal on Computing, INFORMS, vol. 12(4), pages 272-283, November.
    19. Fan, Shu-Kai S. & Zahara, Erwie, 2007. "A hybrid simplex search and particle swarm optimization for unconstrained optimization," European Journal of Operational Research, Elsevier, vol. 181(2), pages 527-548, September.
    20. Hachicha, Wafik & Ammeri, Ahmed & Masmoudi, Faouzi & Chachoub, Habib, 2010. "A comprehensive literature classification of simulation optimisation methods," MPRA Paper 27652, University Library of Munich, Germany.

    More about this item

    Statistics

    Access and download statistics

    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:ejores:v:174:y:2006:i:3:p:1802-1815. 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/locate/eor .

    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.