An Energy-Efficient Thrust Allocation Based on the Improved Dung Beetle Optimizer for the Dynamic Positioning System of Vessels
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| Publicado no: | Journal of Marine Science and Engineering vol. 13, no. 6 (2025), p. 1041-1060 |
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| Autor principal: | |
| Outros Autores: | , , , |
| Publicado em: |
MDPI AG
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| Acesso em linha: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231479 |2 nlm | ||
| 100 | 1 | |a Tuo Yulong |u College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China; tuoyulong@dlmu.edu.cn (Y.T.); linjianlong@dlmu.edu.cn (J.L.) | |
| 245 | 1 | |a An Energy-Efficient Thrust Allocation Based on the Improved Dung Beetle Optimizer for the Dynamic Positioning System of Vessels | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This paper investigates the constrained nonlinear thrust allocation problem for the dynamic positioning system of vessels. Considering the wear, energy consumption, and allocation error of thrusters, a constrained nonlinear mathematical optimization model of thrust allocation is established based on the “Hai Yang Shi You 201”. Based on the dung beetle optimizer (DBO) algorithm, a hybrid Osprey adaptive t-distribution DBO (HOATDBO) algorithm is presented to achieve the thrust allocation. The HOATDBO algorithm introduces the global exploration strategy of the Osprey algorithm, with the addition of the initialization of the good point set and adaptive t-distribution perturbations. The proposed HOATDBO algorithm has perfect global and local optimization capabilities, which can quickly and reliably obtain the optimal thrust solution, improve the thrust allocation accuracy of vessels, and reduce energy consumption. Finally, the simulation and comparison results are presented to verify the superiority of the proposed HOATDBO algorithm. | |
| 653 | |a Accuracy | ||
| 653 | |a Mathematical analysis | ||
| 653 | |a Algorithms | ||
| 653 | |a Beetles | ||
| 653 | |a Energy efficiency | ||
| 653 | |a Thrusters | ||
| 653 | |a Dung | ||
| 653 | |a Vessels | ||
| 653 | |a Foraging behavior | ||
| 653 | |a Energy consumption | ||
| 653 | |a Optimization models | ||
| 653 | |a Machine learning | ||
| 653 | |a Positioning systems | ||
| 653 | |a Dynamic positioning | ||
| 653 | |a Energy | ||
| 653 | |a Local optimization | ||
| 653 | |a Constraints | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Probability distribution | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Lin, Jianlong |u College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China; tuoyulong@dlmu.edu.cn (Y.T.); linjianlong@dlmu.edu.cn (J.L.) | |
| 700 | 1 | |a Peng Zhouhua |u College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China; tuoyulong@dlmu.edu.cn (Y.T.); linjianlong@dlmu.edu.cn (J.L.) | |
| 700 | 1 | |a Wang, Yuanhui |u College of Intelligent System Science and Engineering, Harbin Engineering University, Harbin 150001, China; wangyuanhui@hrbeu.edu.cn | |
| 700 | 1 | |a Wang, Shasha |u College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China; tuoyulong@dlmu.edu.cn (Y.T.); linjianlong@dlmu.edu.cn (J.L.) | |
| 773 | 0 | |t Journal of Marine Science and Engineering |g vol. 13, no. 6 (2025), p. 1041-1060 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3223914890/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3223914890/fulltextwithgraphics/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3223914890/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |