IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v16y2024i4p124-d1371393.html
   My bibliography  Save this article

Task Allocation of Heterogeneous Multi-Unmanned Systems Based on Improved Sheep Flock Optimization Algorithm

Author

Listed:
  • Haibo Liu

    (College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
    Science and Technology on Underwater Vehicle Technology Laboratory, Harbin Engineering University, Harbin 150001, China)

  • Yang Liao

    (College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
    Science and Technology on Underwater Vehicle Technology Laboratory, Harbin Engineering University, Harbin 150001, China)

  • Changting Shi

    (College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
    Science and Technology on Underwater Vehicle Technology Laboratory, Harbin Engineering University, Harbin 150001, China)

  • Jing Shen

    (College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China)

Abstract

The objective of task allocation in unmanned systems is to complete tasks at minimal costs. However, the current algorithms employed for coordinating multiple unmanned systems in task allocation tasks frequently converge to local optima, thus impeding the identification of the best solutions. To address these challenges, this study builds upon the sheep flock optimization algorithm (SFOA) by preserving individuals eliminated during the iterative process within a prior knowledge set, which is continuously updated. During the reproduction phase of the algorithm, this prior knowledge is utilized to guide the generation of new individuals, preventing their rapid reconvergence to local optima. This approach aids in reducing the frequency at which the algorithm converges to local optima, continually steering the algorithm towards the global optimum and thereby enhancing the efficiency of task allocation. Finally, various task scenarios are presented to evaluate the performances of various algorithms. The results show that the algorithm proposed in this paper is more likely than other algorithms to escape from local optima and find the global optimum.

Suggested Citation

  • Haibo Liu & Yang Liao & Changting Shi & Jing Shen, 2024. "Task Allocation of Heterogeneous Multi-Unmanned Systems Based on Improved Sheep Flock Optimization Algorithm," Future Internet, MDPI, vol. 16(4), pages 1-20, April.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:4:p:124-:d:1371393
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/16/4/124/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/16/4/124/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fesanghary, M. & Ardehali, M.M., 2009. "A novel meta-heuristic optimization methodology for solving various types of economic dispatch problem," Energy, Elsevier, vol. 34(6), pages 757-766.
    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. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Narimani, Mohammad Rasoul, 2012. "Reserve constrained dynamic optimal power flow subject to valve-point effects, prohibited zones and multi-fuel constraints," Energy, Elsevier, vol. 47(1), pages 451-464.
    2. Esmaili, Masoud & Shayanfar, Heidar Ali & Amjady, Nima, 2009. "Multi-objective congestion management incorporating voltage and transient stabilities," Energy, Elsevier, vol. 34(9), pages 1401-1412.
    3. Panigrahi, B.K. & Ravikumar Pandi, V. & Das, Sanjoy & Das, Swagatam, 2010. "Multiobjective fuzzy dominance based bacterial foraging algorithm to solve economic emission dispatch problem," Energy, Elsevier, vol. 35(12), pages 4761-4770.
    4. Modiri-Delshad, Mostafa & Aghay Kaboli, S. Hr. & Taslimi-Renani, Ehsan & Rahim, Nasrudin Abd, 2016. "Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options," Energy, Elsevier, vol. 116(P1), pages 637-649.
    5. Alsumait, J.S. & Sykulski, J.K. & Al-Othman, A.K., 2010. "A hybrid GA-PS-SQP method to solve power system valve-point economic dispatch problems," Applied Energy, Elsevier, vol. 87(5), pages 1773-1781, May.
    6. Hossein Lotfi & Mohammad Hasan Nikkhah, 2023. "Presenting a Novel Evolutionary Method for Reserve Constrained Multi-Area Economic/Emission Dispatch Problem," Sustainability, MDPI, vol. 15(13), pages 1-20, July.
    7. Younes, Mimoun & Khodja, Fouad & Kherfane, Riad Lakhdar, 2014. "Multi-objective economic emission dispatch solution using hybrid FFA (firefly algorithm) and considering wind power penetration," Energy, Elsevier, vol. 67(C), pages 595-606.
    8. Chen, Xu & Tang, Guowei, 2022. "Solving static and dynamic multi-area economic dispatch problems using an improved competitive swarm optimization algorithm," Energy, Elsevier, vol. 238(PC).
    9. Yaşar, Celal & Özyön, Serdar, 2011. "A new hybrid approach for nonconvex economic dispatch problem with valve-point effect," Energy, Elsevier, vol. 36(10), pages 5838-5845.
    10. Mahvi, M. & Ardehali, M.M., 2011. "Optimal bidding strategy in a competitive electricity market based on agent-based approach and numerical sensitivity analysis," Energy, Elsevier, vol. 36(11), pages 6367-6374.
    11. Elsakaan, Asmaa A. & El-Sehiemy, Ragab A. & Kaddah, Sahar S. & Elsaid, Mohammed I., 2018. "An enhanced moth-flame optimizer for solving non-smooth economic dispatch problems with emissions," Energy, Elsevier, vol. 157(C), pages 1063-1078.
    12. Sharifian, Yeganeh & Abdi, Hamdi, 2023. "Solving multi-area economic dispatch problem using hybrid exchange market algorithm with grasshopper optimization algorithm," Energy, Elsevier, vol. 267(C).
    13. Shi, Bin & Yan, Lie-Xiang & Wu, Wei, 2013. "Multi-objective optimization for combined heat and power economic dispatch with power transmission loss and emission reduction," Energy, Elsevier, vol. 56(C), pages 135-143.
    14. Mohammadian, M. & Lorestani, A. & Ardehali, M.M., 2018. "Optimization of single and multi-areas economic dispatch problems based on evolutionary particle swarm optimization algorithm," Energy, Elsevier, vol. 161(C), pages 710-724.
    15. Nazari, M.E. & Ardehali, M.M. & Jafari, S., 2010. "Pumped-storage unit commitment with considerations for energy demand, economics, and environmental constraints," Energy, Elsevier, vol. 35(10), pages 4092-4101.
    16. Secui, Dinu Calin, 2016. "A modified Symbiotic Organisms Search algorithm for large scale economic dispatch problem with valve-point effects," Energy, Elsevier, vol. 113(C), pages 366-384.
    17. Liu, Liwei & Sun, Xiaoru & Chen, Chuxiang & Zhao, Erdong, 2016. "How will auctioning impact on the carbon emission abatement cost of electric power generation sector in China?," Applied Energy, Elsevier, vol. 168(C), pages 594-609.
    18. Sharifian, Yeganeh & Abdi, Hamdi, 2024. "Multi-area economic dispatch problem: Methods, uncertainties, and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    19. Guojiang Xiong & Jing Zhang & Xufeng Yuan & Dongyuan Shi & Yu He & Yao Yao & Gonggui Chen, 2018. "A Novel Method for Economic Dispatch with Across Neighborhood Search: A Case Study in a Provincial Power Grid, China," Complexity, Hindawi, vol. 2018, pages 1-18, November.
    20. Wei Sun & Jingmin Wang & Hong Chang, 2012. "Forecasting Annual Power Generation Using a Harmony Search Algorithm-Based Joint Parameters Optimization Combination Model," Energies, MDPI, vol. 5(10), pages 1-24, October.

    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:jftint:v:16:y:2024:i:4:p:124-:d:1371393. 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.