IDEAS home Printed from https://ideas.repec.org/a/hin/jjopti/3260940.html
   My bibliography  Save this article

Hybridization of Adaptive Differential Evolution with an Expensive Local Search Method

Author

Listed:
  • Rashida Adeeb Khanum
  • Muhammad Asif Jan
  • Nasser Mansoor Tairan
  • Wali Khan Mashwani

Abstract

Differential evolution (DE) is an effective and efficient heuristic for global optimization problems. However, it faces difficulty in exploiting the local region around the approximate solution. To handle this issue, local search (LS) techniques could be hybridized with DE to improve its local search capability. In this work, we hybridize an updated version of DE, adaptive differential evolution with optional external archive (JADE) with an expensive LS method, Broydon-Fletcher-Goldfarb-Shano (BFGS) for solving continuous unconstrained global optimization problems. The new hybrid algorithm is denoted by DEELS. To validate the performance of DEELS, we carried out extensive experiments on well known test problems suits, CEC2005 and CEC2010. The experimental results, in terms of function error values, success rate, and some other statistics, are compared with some of the state-of-the-art algorithms, self-adaptive control parameters in differential evolution (jDE), sequential DE enhanced by neighborhood search for large-scale global optimization (SDENS), and differential ant-stigmergy algorithm (DASA). These comparisons reveal that DEELS outperforms jDE and SDENS except DASA on the majority of test instances.

Suggested Citation

  • Rashida Adeeb Khanum & Muhammad Asif Jan & Nasser Mansoor Tairan & Wali Khan Mashwani, 2016. "Hybridization of Adaptive Differential Evolution with an Expensive Local Search Method," Journal of Optimization, Hindawi, vol. 2016, pages 1-14, July.
  • Handle: RePEc:hin:jjopti:3260940
    DOI: 10.1155/2016/3260940
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/7179/2016/3260940.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/7179/2016/3260940.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2016/3260940?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
    ---><---

    References listed on IDEAS

    as
    1. Kaelo, P. & Ali, M.M., 2006. "A numerical study of some modified differential evolution algorithms," European Journal of Operational Research, Elsevier, vol. 169(3), pages 1176-1184, March.
    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. M. Ali & W. Zhu, 2013. "A penalty function-based differential evolution algorithm for constrained global optimization," Computational Optimization and Applications, Springer, vol. 54(3), pages 707-739, April.
    2. Zio, E. & Viadana, G., 2011. "Optimization of the inspection intervals of a safety system in a nuclear power plant by Multi-Objective Differential Evolution (MODE)," Reliability Engineering and System Safety, Elsevier, vol. 96(11), pages 1552-1563.
    3. Piotrowski, Adam P. & Napiorkowski, Jaroslaw J. & Kiczko, Adam, 2012. "Differential Evolution algorithm with Separated Groups for multi-dimensional optimization problems," European Journal of Operational Research, Elsevier, vol. 216(1), pages 33-46.
    4. du Plessis, Mathys C. & Engelbrecht, Andries P., 2012. "Using Competitive Population Evaluation in a differential evolution algorithm for dynamic environments," European Journal of Operational Research, Elsevier, vol. 218(1), pages 7-20.
    5. Biswas (Raha), Syamasree & Mandal, Kamal Krishna & Chakraborty, Niladri, 2016. "Pareto-efficient double auction power transactions for economic reactive power dispatch," Applied Energy, Elsevier, vol. 168(C), pages 610-627.
    6. Andreas C. Nearchou & Sotiris L. Omirou, 2024. "Self-Adaptive Biased Differential Evolution for Scheduling Against Common Due Dates," SN Operations Research Forum, Springer, vol. 5(2), pages 1-29, June.
    7. Kaelo, P. & Ali, M.M., 2007. "Integrated crossover rules in real coded genetic algorithms," European Journal of Operational Research, Elsevier, vol. 176(1), pages 60-76, January.
    8. Maysam Safe & Seyed Khazraee & Payam Setoodeh & Abdolhosein Jahanmiri, 2013. "Model reduction and optimization of a reactive dividing wall batch distillation column inspired by response surface methodology and differential evolution," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 19(1), pages 29-50.
    9. Mohsen Davoodi & Hamed Jafari Kaleybar & Morris Brenna & Dario Zaninelli, 2023. "Energy Management Systems for Smart Electric Railway Networks: A Methodological Review," Sustainability, MDPI, vol. 15(16), pages 1-35, August.
    10. Ali, Musrrat. & Siarry, Patrick & Pant, Millie., 2012. "An efficient Differential Evolution based algorithm for solving multi-objective optimization problems," European Journal of Operational Research, Elsevier, vol. 217(2), pages 404-416.
    11. Andreas C. Nearchou, 2018. "Multicriteria scheduling optimization using an elitist multiobjective population heuristic: the h-NSDE algorithm," Journal of Heuristics, Springer, vol. 24(6), pages 817-851, December.
    12. Pravesh Kumar & Millie Pant, 2018. "Recognition of noise source in multi sounds field by modified random localized based DE algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(1), pages 245-261, February.
    13. Ali, M.M., 2007. "Differential evolution with preferential crossover," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1137-1147, September.

    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:hin:jjopti:3260940. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.