Multi-Unmanned Aerial Vehicle Path Planning Based on Improved Nutcracker Optimization Algorithm

For the multi-UAV path planning problem, environmental modeling and an improved swarm intelligence-based optimization algorithm are discussed in this paper. Firstly, to align with reality, specific constraints of UAVs in motions, attitudes and altitudes, real-world threats such as radars and no-fly...

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Veröffentlicht in:Drones (Basel) Jg. 9; H. 2; S. 116
Hauptverfasser: Xiao, Chang, Yang, Huiliao, Zhang, Bo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.02.2025
Schlagworte:
Algorithms
Convergence
Deep learning
Energy consumption
Environment models
Exploitation
Fines & penalties
improved nutcracker optimization algorithm
Mathematical models
Mountains
multi-UAV
Multiple objective analysis
Optimization
Optimization algorithms
Path planning
Swarm intelligence
Threats
Unmanned aerial vehicles
ISSN:2504-446X, 2504-446X
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Zusammenfassung:For the multi-UAV path planning problem, environmental modeling and an improved swarm intelligence-based optimization algorithm are discussed in this paper. Firstly, to align with reality, specific constraints of UAVs in motions, attitudes and altitudes, real-world threats such as radars and no-fly zones, and inter-UAV collisions are considered. Thus, multi-UAV path planning is transformed into a multi-objective constrained optimization problem. Accordingly, an improved nutcracker optimization algorithm is proposed to solve this problem. Through initializing with logistic chaotic mapping and the lens imaging inverse learning strategy, a more fit elite initialization population is produced to increase the efficiency of path planning. Furthermore, by adjusting adaptive parameters and integrating an improved sine-cosine search strategy, a balance between global exploration capability and local exploitation capability during path planning is achieved. Experimental results show that the improved nutcracker optimization algorithm surpasses other algorithms with respect to both convergence speed and convergence value, making it an effective method for multi-UAV path planning.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:2504-446X
2504-446X
DOI:10.3390/drones9020116