IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v209y2023icp55-86.html
   My bibliography  Save this article

A novel reinforcement learning based tuna swarm optimization algorithm for autonomous underwater vehicle path planning

Author

Listed:
  • Yan, Zheping
  • Yan, Jinyu
  • Wu, Yifan
  • Cai, Sijia
  • Wang, Hongxing

Abstract

Path planning technology is an important guarantee for the safe navigation of autonomous underwater vehicle (AUV) in water, and it is also an important indicator of the intelligence of autonomous underwater vehicle. Aiming at the path planning problem of AUV in complex environments, this article presents a reinforcement learning-based tuna swarm optimization algorithm called the QLTSO. In this algorithm, individuals are independent of each other, and the choice of each individual strategy is decided by reinforcement learning. Four strategies are set for each individual in the algorithm: spiral foraging, parabolic foraging, optimization adjustment and ESOS strategy. Finally, the cubic B-spline curve is used to smooth the path so that the autonomous underwater vehicle can better track the path. To verify the superiority of the QLTSO algorithm, the algorithm is compared with other advanced optimization algorithms. The simulation results show that the QLTSO algorithm can plan safe and effective AUV navigation paths in a variety of two-dimensional and three-dimensional complex environments with better convergence and robustness, and the planning success rate is up to 100%, which is an effective AUV path planning algorithm.

Suggested Citation

  • Yan, Zheping & Yan, Jinyu & Wu, Yifan & Cai, Sijia & Wang, Hongxing, 2023. "A novel reinforcement learning based tuna swarm optimization algorithm for autonomous underwater vehicle path planning," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 209(C), pages 55-86.
    • Handle: RePEc:eee:matcom:v:209:y:2023:i:c:p:55-86
    DOI: 10.1016/j.matcom.2023.02.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475423000629
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2023.02.003?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. Haoqian Huang & Chao Jin & Hassan Zargarzadeh, 2021. "A Novel Particle Swarm Optimization Algorithm Based on Reinforcement Learning Mechanism for AUV Path Planning," Complexity, Hindawi, vol. 2021, pages 1-13, December.
    2. Fister, Iztok & Iglesias, Andres & Galvez, Akemi & Del Ser, Javier & Osaba, Eneko & Fister, Iztok & Perc, Matjaž & Slavinec, Mitja, 2019. "Novelty search for global optimization," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 865-881.
    3. Yan, Zheping & Zhang, Jinzhong & Tang, Jialing, 2021. "Path planning for autonomous underwater vehicle based on an enhanced water wave optimization algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 181(C), pages 192-241.
    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. Yu Chang & Dengxu He & Liangdong Qu, 2024. "An improved tuna swarm optimization algorithm based on behavior evaluation for wireless sensor network coverage optimization," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 86(4), pages 829-851, August.

    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. Mehmood, Ammara & Raja, Muhammad Asif Zahoor & Ninness, Brett, 2024. "Design of fractional-order hammerstein control auto-regressive model for heat exchanger system identification: Treatise on fuzzy-evolutionary computing," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    2. Brennan McCann & Morad Nazari & Christopher Petersen, 2024. "Numerical Approaches for Constrained and Unconstrained, Static Optimization on the Special Euclidean Group SE(3)," Journal of Optimization Theory and Applications, Springer, vol. 201(3), pages 1116-1150, June.
    3. Yan, Zheping & Zhang, Jinzhong & Zeng, Jia & Tang, Jialing, 2021. "Nature-inspired approach: An enhanced whale optimization algorithm for global optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 17-46.
    4. Kutlu Onay, Funda & Aydemı̇r, Salih Berkan, 2022. "Chaotic hunger games search optimization algorithm for global optimization and engineering problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 514-536.
    5. Qiyi He & Jin Tu & Zhiwei Ye & Mingwei Wang & Ye Cao & Xianjing Zhou & Wanfang Bai, 2023. "Association Rule Mining through Combining Hybrid Water Wave Optimization Algorithm with Levy Flight," Mathematics, MDPI, vol. 11(5), pages 1-19, February.
    6. Yu, Caiyang & Cai, Zhennao & Ye, Xiaojia & Wang, Mingjing & Zhao, Xuehua & Liang, Guoxi & Chen, Huiling & Li, Chengye, 2020. "Quantum-like mutation-induced dragonfly-inspired optimization approach," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 178(C), pages 259-289.

    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:matcom:v:209:y:2023:i:c:p:55-86. 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: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    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.