IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i18p3405-d919004.html
   My bibliography  Save this article

CLTSA: A Novel Tunicate Swarm Algorithm Based on Chaotic-Lévy Flight Strategy for Solving Optimization Problems

Author

Listed:
  • Yi Cui

    (School of Computer Science and Engineering, Central South University, Changsha 410083, China)

  • Ronghua Shi

    (School of Computer Science and Engineering, Central South University, Changsha 410083, China)

  • Jian Dong

    (School of Computer Science and Engineering, Central South University, Changsha 410083, China)

Abstract

In this paper, we proposed a tunicate swarm algorithm based on Tent-Lévy flight (TLTSA) to avoid converging prematurely or failing to escape from a local optimal solution. First, we combined nine chaotic maps with the Lévy flight strategy to obtain nine different TSAs based on a Chaotic-Lévy flight strategy (CLTSA). Experimental results demonstrated that a TSA based on Tent-Lévy flight (TLTSA) performed the best among nine CLTSAs. Afterwards, the TLTSA was selected for comparative research with other well-known meta-heuristic algorithms. The 16 unimodal benchmark functions, 14 multimodal benchmark functions, 6 fixed-dimension functions, and 3 constrained practical problems in engineering were selected to verify the performance of TLTSA. The results of the test functions suggested that the TLTSA was better than the TSA and other algorithms in searching for global optimal solutions because of its excellent exploration and exploitation capabilities. Finally, the engineering experiments also demonstrated that a TLTSA solved constrained practical engineering problems more effectively.

Suggested Citation

  • Yi Cui & Ronghua Shi & Jian Dong, 2022. "CLTSA: A Novel Tunicate Swarm Algorithm Based on Chaotic-Lévy Flight Strategy for Solving Optimization Problems," Mathematics, MDPI, vol. 10(18), pages 1-39, September.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:18:p:3405-:d:919004
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/18/3405/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/18/3405/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. E Emary & Hossam M Zawbaa, 2016. "Impact of Chaos Functions on Modern Swarm Optimizers," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-26, 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. Jian Dong, 2023. "Preface to the Special Issue on “Recent Advances in Swarm Intelligence Algorithms and Their Applications”—Special Issue Book," Mathematics, MDPI, vol. 11(12), pages 1-4, June.

    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. Esteban Tlelo-Cuautle & Antonio de Jesus Quintas-Valles & Luis Gerardo de la Fraga & Jose de Jesus Rangel-Magdaleno, 2016. "VHDL Descriptions for the FPGA Implementation of PWL-Function-Based Multi-Scroll Chaotic Oscillators," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-32, December.
    2. Elena Niculina Dragoi & Vlad Dafinescu, 2021. "Review of Metaheuristics Inspired from the Animal Kingdom," Mathematics, MDPI, vol. 9(18), pages 1-52, September.
    3. Abdelhady Ramadan & Salah Kamel & Mohamed H. Hassan & Marcos Tostado-Véliz & Ali M. Eltamaly, 2021. "Parameter Estimation of Static/Dynamic Photovoltaic Models Using a Developed Version of Eagle Strategy Gradient-Based Optimizer," Sustainability, MDPI, vol. 13(23), pages 1-29, November.
    4. Shinohara, Shuji & Okamoto, Hiroshi & Manome, Nobuhito & Gunji, Pegio-Yukio & Nakajima, Yoshihiro & Moriyama, Toru & Chung, Ung-il, 2022. "Simulation of foraging behavior using a decision-making agent with Bayesian and inverse Bayesian inference: Temporal correlations and power laws in displacement patterns," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).

    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:gam:jmathe:v:10:y:2022:i:18:p:3405-:d:919004. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.