Learning Improvement Heuristics for Multi-Unmanned Aerial Vehicle Task Allocation
Nowadays, small UAV swarms with the capability of carrying inexpensive munitions have been highly effective in strike missions against ground targets on the battlefield. Effective task allocation is crucial for improving the overall operational effectiveness of these UAV swarms. Traditional heuristi...
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MDPI AG
2024-11-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/8/11/636 |
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| author | Boyang Fan Yuming Bo Xiang Wu |
| author_facet | Boyang Fan Yuming Bo Xiang Wu |
| author_sort | Boyang Fan |
| collection | DOAJ |
| description | Nowadays, small UAV swarms with the capability of carrying inexpensive munitions have been highly effective in strike missions against ground targets on the battlefield. Effective task allocation is crucial for improving the overall operational effectiveness of these UAV swarms. Traditional heuristic methods for addressing the task allocation problem often rely on handcrafted rules, which may limit their performance for the complicated tasks. In this paper, a NeuroSelect Discrete Particle Swarm Optimization (NSDPSO) algorithm is presented for the Multi-UAV Task Allocation (MUTA) problem. Specifically, a Transformer-based model is proposed to learn design NeuroSelect Heuristic for DPSO to improve the evolutionary process. The iteration of DPSO is modeled as a decomposed Markov Decision Process (MDP), and a reinforcement learning algorithm is employed to train the network parameters. The simulation results are provided to verify the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-ff1a5de74cf541bfaa8ae187112c0fe0 |
| institution | OA Journals |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-ff1a5de74cf541bfaa8ae187112c0fe02025-08-20T02:08:03ZengMDPI AGDrones2504-446X2024-11-0181163610.3390/drones8110636Learning Improvement Heuristics for Multi-Unmanned Aerial Vehicle Task AllocationBoyang Fan0Yuming Bo1Xiang Wu2School of Automation, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Automation, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Automation, Nanjing University of Science and Technology, Nanjing 210094, ChinaNowadays, small UAV swarms with the capability of carrying inexpensive munitions have been highly effective in strike missions against ground targets on the battlefield. Effective task allocation is crucial for improving the overall operational effectiveness of these UAV swarms. Traditional heuristic methods for addressing the task allocation problem often rely on handcrafted rules, which may limit their performance for the complicated tasks. In this paper, a NeuroSelect Discrete Particle Swarm Optimization (NSDPSO) algorithm is presented for the Multi-UAV Task Allocation (MUTA) problem. Specifically, a Transformer-based model is proposed to learn design NeuroSelect Heuristic for DPSO to improve the evolutionary process. The iteration of DPSO is modeled as a decomposed Markov Decision Process (MDP), and a reinforcement learning algorithm is employed to train the network parameters. The simulation results are provided to verify the effectiveness of the proposed method.https://www.mdpi.com/2504-446X/8/11/636unmanned aerial vehicle (UAV)multi-UAV task allocationdiscrete particle swarm optimizationreinforcement learningtransformer |
| spellingShingle | Boyang Fan Yuming Bo Xiang Wu Learning Improvement Heuristics for Multi-Unmanned Aerial Vehicle Task Allocation Drones unmanned aerial vehicle (UAV) multi-UAV task allocation discrete particle swarm optimization reinforcement learning transformer |
| title | Learning Improvement Heuristics for Multi-Unmanned Aerial Vehicle Task Allocation |
| title_full | Learning Improvement Heuristics for Multi-Unmanned Aerial Vehicle Task Allocation |
| title_fullStr | Learning Improvement Heuristics for Multi-Unmanned Aerial Vehicle Task Allocation |
| title_full_unstemmed | Learning Improvement Heuristics for Multi-Unmanned Aerial Vehicle Task Allocation |
| title_short | Learning Improvement Heuristics for Multi-Unmanned Aerial Vehicle Task Allocation |
| title_sort | learning improvement heuristics for multi unmanned aerial vehicle task allocation |
| topic | unmanned aerial vehicle (UAV) multi-UAV task allocation discrete particle swarm optimization reinforcement learning transformer |
| url | https://www.mdpi.com/2504-446X/8/11/636 |
| work_keys_str_mv | AT boyangfan learningimprovementheuristicsformultiunmannedaerialvehicletaskallocation AT yumingbo learningimprovementheuristicsformultiunmannedaerialvehicletaskallocation AT xiangwu learningimprovementheuristicsformultiunmannedaerialvehicletaskallocation |