IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v282y2016icp65-83.html
   My bibliography  Save this article

Continuous global optimization through the generation of parametric curves

Author

Listed:
  • Ziadi, Raouf
  • Bencherif-Madani, Abdelatif
  • Ellaia, Rachid

Abstract

In this paper we develop a new approach for solving a large class of global optimization problems. The objective function is only continuous, non-smooth and non-Lipschitzian, defined on a rectangle of Rn. This approach is based on the generation, in the feasible set, of a family of parametrized curves satisfying certain properties combined with the one-dimensional Evtushenko algorithm. To accelerate the corresponding mixed algorithm, we have incorporated in a variant a Pattern Search-type deterministic local optimization method and in another variant a new stochastic local optimization method. Both variants have been applied to several typical test problems. A comparison with some well known methods is highlighted through numerical experiments.

Suggested Citation

  • Ziadi, Raouf & Bencherif-Madani, Abdelatif & Ellaia, Rachid, 2016. "Continuous global optimization through the generation of parametric curves," Applied Mathematics and Computation, Elsevier, vol. 282(C), pages 65-83.
    • Handle: RePEc:eee:apmaco:v:282:y:2016:i:c:p:65-83
    DOI: 10.1016/j.amc.2016.01.067
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300316300765
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2016.01.067?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. Mishra, Sudhanshu, 2006. "Some new test functions for global optimization and performance of repulsive particle swarm method," MPRA Paper 2718, University Library of Munich, Germany.
    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. Ziadi, Raouf & Bencherif-Madani, Abdelatif & Ellaia, Rachid, 2020. "A deterministic method for continuous global optimization using a dense curve," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 178(C), pages 62-91.
    2. Naffeti, Bechir & Ammar, Hamadi, 2021. "A new trisection method for solving Lipschitz bi-objective optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 1186-1205.

    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. Mishra, SK, 2006. "Performance of Differential Evolution and Particle Swarm Methods on Some Relatively Harder Multi-modal Benchmark Functions," MPRA Paper 449, University Library of Munich, Germany.
    2. Weitao Sun & Yuan Dong, 2011. "Study of multiscale global optimization based on parameter space partition," Journal of Global Optimization, Springer, vol. 49(1), pages 149-172, January.
    3. Mehmet Hakan Satman & Emre Akadal, 2020. "Machine Coded Compact Genetic Algorithms for Real Parameter Optimization Problems," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 8(1), pages 43-58, June.
    4. Linas Stripinis & Remigijus Paulavičius, 2022. "Experimental Study of Excessive Local Refinement Reduction Techniques for Global Optimization DIRECT-Type Algorithms," Mathematics, MDPI, vol. 10(20), pages 1-18, October.
    5. Timothy Haas, 2020. "Developing political-ecological theory: The need for many-task computing," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-26, November.
    6. Massimiliano Kaucic, 2013. "A multi-start opposition-based particle swarm optimization algorithm with adaptive velocity for bound constrained global optimization," Journal of Global Optimization, Springer, vol. 55(1), pages 165-188, January.
    7. Mishra, SK, 2012. "Global optimization of some difficult benchmark functions by cuckoo-hostco-evolution meta-heuristics," MPRA Paper 40615, University Library of Munich, Germany.
    8. Beopsoo Kim & Nikita Rusetskii & Haesung Jo & Insu Kim, 2021. "The Optimal Allocation of Distributed Generators Considering Fault Current and Levelized Cost of Energy Using the Particle Swarm Optimization Method," Energies, MDPI, vol. 14(2), pages 1-18, January.
    9. Sudhanshu K Mishra, 2013. "Global Optimization of Some Difficult Benchmark Functions by Host-Parasite Coevolutionary Algorithm," Economics Bulletin, AccessEcon, vol. 33(1), pages 1-18.

    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:apmaco:v:282:y:2016:i:c:p:65-83. 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: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    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.