Multi-UAV path planning considering multiple energy consumptions via an improved bee foraging learning particle swarm optimization algorithm Article Swipe
Yuanhang Qi
,
Haoran Jiang
,
Gewen Huang
,
Liang Yang
,
Fujie Wang
,
Yunjian Xu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-99001-z
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-99001-z
With the advancement of unmanned aerial vehicle (UAV) technology, UAVs, such as multi-rotor drones, have found widespread application in wireless sensor networks. In scenarios where multiple UAVs collaborate to gather sensor data from the field, it is essential to establish a path planning model that incorporates an accurate energy consumption model for these UAVs. The power consumption of a multi-rotor drone varies depending on its flight state. When UAVs traverse various locations, it is not only the power required for steady-level flight that must be considered, but also the power necessary for acceleration, deceleration, climbing, and turning. This paper presents a path planning model for multiple UAVs, termed the Multi-UAV Path Planning Considering Multiple Energy Consumptions (MUAVPP-MEC). The solution derived adheres to the constraint that UAV flight energy consumption should not exceed the maximum stored energy, with the goal of minimizing the total flight time across all UAV paths. To tackle the MUAVPP-MEC, this study proposes an improved Bee Foraging Learning Particle Swarm Optimization algorithm (IBFLPSO), which integrates the bee-foraging algorithm into the particle swarm optimization framework. The IBFLPSO facilitates an efficient real-number encoding and greedy segmenting sequence decoding strategy, translating the solution space of the problem into the search space of the algorithm. To improve the optimization capabilities of the algorithm, IBFLPSO utilizes the energy-constrained 2-opt as a local search operator. In Experiment 1, the proposed model and algorithm are validated through three distinct case studies, demonstrating the stability and efficacy of the methods. It is clearly observed that as the number of collection points increases, both the total cruising time and energy consumption of the model rise significantly, thus confirming the accuracy of the model. In Experiment 2, when compared with four other algorithms, IBFLPSO outperforms them in both the optimal and average solutions. Specifically, the optimal solution of IBFLPSO is 54.64%, 49.45%, 25.78%, and 22.92% better than those of the traditional PSO algorithm, PSO-2OPT algorithm, GA, and BFLPSO, respectively.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-025-99001-z
- https://www.nature.com/articles/s41598-025-99001-z.pdf
- OA Status
- gold
- Cited By
- 4
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409858931
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- OpenAlex ID
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https://openalex.org/W4409858931Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s41598-025-99001-zDigital Object Identifier
- Title
-
Multi-UAV path planning considering multiple energy consumptions via an improved bee foraging learning particle swarm optimization algorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-04-28Full publication date if available
- Authors
-
Yuanhang Qi, Haoran Jiang, Gewen Huang, Liang Yang, Fujie Wang, Yunjian XuList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-025-99001-zPublisher landing page
- PDF URL
-
https://www.nature.com/articles/s41598-025-99001-z.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.nature.com/articles/s41598-025-99001-z.pdfDirect OA link when available
- Concepts
-
Foraging, Particle swarm optimization, Computer science, Mathematical optimization, Swarm behaviour, Path (computing), Artificial bee colony algorithm, Energy (signal processing), Motion planning, Algorithm, Artificial intelligence, Biology, Mathematics, Ecology, Computer network, Statistics, RobotTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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