IDEAS home Printed from https://ideas.repec.org/a/spr/opsear/v54y2017i4d10.1007_s12597-017-0302-0.html
   My bibliography  Save this article

Multi-directional bat algorithm for solving unconstrained optimization problems

Author

Listed:
  • Mohamed A. Tawhid

    (Thompson Rivers University
    Alexandria University)

  • Ahmed F. Ali

    (Suez Canal University
    Thompson Rivers University)

Abstract

In this paper, we propose a new hybrid algorithm for solving unconstrained global optimization problems by hybridizing the bat algorithm with multi-directional search algorithm (MDS). We call the proposed algorithm by multi-directional bat algorithm (MDBAT). In MDBAT algorithm, we try to overcome the slow convergence of the bat algorithm as a metaheuristic algorithm by invoking one of the promising direct search algorithm which is called MDS algorithm. The bat algorithm has a good ability to make exploration and exploitation search while the MDS has a good ability for accelerating convergence on the region of optimal response. In the beginning, the standard bat algorithm starts the search for number of iterations then the MDS algorithm starts its search from bat algorithm found so far. The combination between the standard bat algorithm and the MDS algorithm helps the MDS algorithm to start the search from a good solution instead of the random initial solution. The MDS algorithm can accelerate the search of the proposed algorithm instead of letting the algorithm running for more iterations without any improvement. We investigate the general performance of the MDBAT algorithm by applying it on 16 unconstrained global optimization problems and comparing it against 8 benchmark algorithms. The experimental results indicate that MDBAT is a promising algorithm and outperforms the other algorithms in most cases.

Suggested Citation

  • Mohamed A. Tawhid & Ahmed F. Ali, 2017. "Multi-directional bat algorithm for solving unconstrained optimization problems," OPSEARCH, Springer;Operational Research Society of India, vol. 54(4), pages 684-705, December.
    • Handle: RePEc:spr:opsear:v:54:y:2017:i:4:d:10.1007_s12597-017-0302-0
    DOI: 10.1007/s12597-017-0302-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12597-017-0302-0
    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/s12597-017-0302-0?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. Mohamed A. Tawhid & Ahmed F. Ali, 2016. "Simplex particle swarm optimization with arithmetical crossover for solving global optimization problems," OPSEARCH, Springer;Operational Research Society of India, vol. 53(4), pages 705-740, December.
    2. Y. Petalas & K. Parsopoulos & M. Vrahatis, 2007. "Memetic particle swarm optimization," Annals of Operations Research, Springer, vol. 156(1), pages 99-127, December.
    3. Millie Pant & Radha Thangaraj & Ajith Abraham, 2011. "De-Pso: A New Hybrid Meta-Heuristic For Solving Global Optimization Problems," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 7(03), pages 363-381.
    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. Abdelmonem M. Ibrahim & Mohamed A. Tawhid, 2023. "An improved artificial algae algorithm integrated with differential evolution for job-shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1763-1778, April.
    2. Tawhid, M.A. & Ibrahim, A.M., 2021. "Solving nonlinear systems and unconstrained optimization problems by hybridizing whale optimization algorithm and flower pollination algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 1342-1369.

    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. Ivona Brajević, 2021. "A Shuffle-Based Artificial Bee Colony Algorithm for Solving Integer Programming and Minimax Problems," Mathematics, MDPI, vol. 9(11), pages 1-20, May.
    2. Zhang, Zijun & Kusiak, Andrew & Song, Zhe, 2013. "Scheduling electric power production at a wind farm," European Journal of Operational Research, Elsevier, vol. 224(1), pages 227-238.
    3. Sacchelli, S. & Fabbrizzi, S., 2015. "Minimisation of uncertainty in decision-making processes using optimised probabilistic Fuzzy Cognitive Maps: A case study for a rural sector," Socio-Economic Planning Sciences, Elsevier, vol. 52(C), pages 31-40.
    4. Ana Maria A. C. Rocha & M. Fernanda P. Costa & Edite M. G. P. Fernandes, 2017. "On a smoothed penalty-based algorithm for global optimization," Journal of Global Optimization, Springer, vol. 69(3), pages 561-585, November.
    5. Ana Rocha & M. Costa & Edite Fernandes, 2014. "A filter-based artificial fish swarm algorithm for constrained global optimization: theoretical and practical issues," Journal of Global Optimization, Springer, vol. 60(2), pages 239-263, October.
    6. Khalid Abdulaziz Alnowibet & Salem Mahdi & Mahmoud El-Alem & Mohamed Abdelawwad & Ali Wagdy Mohamed, 2022. "Guided Hybrid Modified Simulated Annealing Algorithm for Solving Constrained Global Optimization Problems," Mathematics, MDPI, vol. 10(8), pages 1-25, April.
    7. Raghav Prasad Parouha & Pooja Verma, 2022. "An innovative hybrid algorithm for bound-unconstrained optimization problems and applications," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1273-1336, June.
    8. Mohamed A. Tawhid & Ahmed F. Ali, 2016. "Simplex particle swarm optimization with arithmetical crossover for solving global optimization problems," OPSEARCH, Springer;Operational Research Society of India, vol. 53(4), pages 705-740, December.
    9. M. Joseane F. G. Macêdo & Elizabeth W. Karas & M. Fernanda P. Costa & Ana Maria A. C. Rocha, 2020. "Filter-based stochastic algorithm for global optimization," Journal of Global Optimization, Springer, vol. 77(4), pages 777-805, August.
    10. Umberto Bartoccini & Arturo Carpi & Valentina Poggioni & Valentino Santucci, 2019. "Memes Evolution in a Memetic Variant of Particle Swarm Optimization," Mathematics, MDPI, vol. 7(5), pages 1-13, May.
    11. Marco Locatelli & Fabio Schoen, 2016. "Global optimization based on local searches," Annals of Operations Research, Springer, vol. 240(1), pages 251-270, May.
    12. Gao, Fei & Gao, Hongrui & Li, Zhuoqiu & Tong, Hengqing & Lee, Ju-Jang, 2009. "Detecting unstable periodic orbits of nonlinear mappings by a novel quantum-behaved particle swarm optimization non-Lyapunov way," Chaos, Solitons & Fractals, Elsevier, vol. 42(4), pages 2450-2463.

    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:opsear:v:54:y:2017:i:4:d:10.1007_s12597-017-0302-0. 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.