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

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  • 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
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    References listed on IDEAS

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    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.
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