IDEAS home Printed from https://ideas.repec.org/a/spr/cejnor/v25y2017i4d10.1007_s10100-017-0470-2.html
   My bibliography  Save this article

Empirical study of the improved UNIRANDI local search method

Author

Listed:
  • László Pál

    (Sapientia - Hungarian University of Transylvania)

Abstract

UNIRANDI is a stochastic local search algorithm that performs line searches from starting points along good random directions. In this paper, we focus on a modified version of this method. The new algorithm, addition to the random directions, considers more promising directions in order to speed up the optimization process. The performance of the new method is tested empirically on standard test functions in terms of function evaluations, success rates, error values, and CPU time. It is also compared to the previous version as well as other local search methods. Numerical results show that the new method is promising in terms of robustness and efficiency.

Suggested Citation

  • László Pál, 2017. "Empirical study of the improved UNIRANDI local search method," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(4), pages 929-952, December.
    • Handle: RePEc:spr:cejnor:v:25:y:2017:i:4:d:10.1007_s10100-017-0470-2
    DOI: 10.1007/s10100-017-0470-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10100-017-0470-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10100-017-0470-2?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. Hirsch, M.J. & Pardalos, P.M. & Resende, M.G.C., 2010. "Speeding up continuous GRASP," European Journal of Operational Research, Elsevier, vol. 205(3), pages 507-521, September.
    2. Luis Rios & Nikolaos Sahinidis, 2013. "Derivative-free optimization: a review of algorithms and comparison of software implementations," Journal of Global Optimization, Springer, vol. 56(3), pages 1247-1293, July.
    3. L. Grippo & F. Rinaldi, 2015. "A class of derivative-free nonmonotone optimization algorithms employing coordinate rotations and gradient approximations," Computational Optimization and Applications, Springer, vol. 60(1), pages 1-33, January.
    4. A. Custódio & H. Rocha & L. Vicente, 2010. "Incorporating minimum Frobenius norm models in direct search," Computational Optimization and Applications, Springer, vol. 46(2), pages 265-278, June.
    5. 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. Tibor Csendes & Csanád Imreh & József Temesi, 2017. "Editorial," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(4), pages 739-741, December.

    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. Y. Diouane & S. Gratton & L. Vicente, 2015. "Globally convergent evolution strategies for constrained optimization," Computational Optimization and Applications, Springer, vol. 62(2), pages 323-346, November.
    2. Christophe Gouel & Nicolas Legrand, 2017. "Estimating the Competitive Storage Model with Trending Commodity Prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(4), pages 744-763, June.
    3. Zhao, Jake, 2020. "Accounting for the corporate cash increase," European Economic Review, Elsevier, vol. 123(C).
    4. 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.
    5. Breitmoser, Yves & Valasek, Justin, 2017. "A rationale for unanimity in committees," Discussion Papers, Research Unit: Economics of Change SP II 2017-308, WZB Berlin Social Science Center.
    6. Tavakol Aghaei, Vahid & Ağababaoğlu, Arda & Bawo, Biram & Naseradinmousavi, Peiman & Yıldırım, Sinan & Yeşilyurt, Serhat & Onat, Ahmet, 2023. "Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm," Applied Energy, Elsevier, vol. 341(C).
    7. 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.
    8. 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.
    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. 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.
    11. 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.
    12. Pál, László & Sándor, Zsolt, 2023. "Comparing procedures for estimating random coefficient logit demand models with a special focus on obtaining global optima," International Journal of Industrial Organization, Elsevier, vol. 88(C).
    13. Qihong Feng & Kuankuan Wu & Jiyuan Zhang & Sen Wang & Xianmin Zhang & Daiyu Zhou & An Zhao, 2022. "Optimization of Well Control during Gas Flooding Using the Deep-LSTM-Based Proxy Model: A Case Study in the Baoshaceng Reservoir, Tarim, China," Energies, MDPI, vol. 15(7), pages 1-14, March.
    14. Luca Riboldi & Lars O. Nord, 2017. "Lifetime Assessment of Combined Cycles for Cogeneration of Power and Heat in Offshore Oil and Gas Installations," Energies, MDPI, vol. 10(6), pages 1-23, May.
    15. 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.
    16. 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.
    17. Li Dai & Patrick Minford & Peng Zhou, 2015. "A DSGE model of China," Applied Economics, Taylor & Francis Journals, vol. 47(59), pages 6438-6460, December.
    18. Khakifirooz, Marzieh & Fathi, Michel & Lee, I-Chen & Tseng, Sheng-Tsaing, 2023. "Neural ordinary differential equation for sequential optimal design of fatigue test under accelerated life test analysis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    19. Gu, Ziyuan & Li, Yifan & Saberi, Meead & Rashidi, Taha H. & Liu, Zhiyuan, 2023. "Macroscopic parking dynamics and equitable pricing: Integrating trip-based modeling with simulation-based robust optimization," Transportation Research Part B: Methodological, Elsevier, vol. 173(C), pages 354-381.
    20. Khalid Mohammed Saffer Alzaidi & Oguz Bayat & Osman N. Uçan, 2018. "A Heuristic Approach for Optimal Planning and Operation of Distribution Systems," Journal of Optimization, Hindawi, vol. 2018, pages 1-19, June.

    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:cejnor:v:25:y:2017:i:4:d:10.1007_s10100-017-0470-2. 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.