Improving the survivability of a reconnaissance UAV swarm based on cellular automata
Abstract
The concept of swarming is not new. It is known to have originated and spread in the biological sciences, for example, bee colonies (reproduction). It has also found its application in military tactics - when several units converge to attack a target from several directions in a deliberately structured, coordinated manner. The paper describes the possibility of integrating bee swarm algorithm into cellular automata. The option of developing a swarm system with such integration in mind is analyzed. The article also describes algorithms for increasing the survivability of a UAV swarm system by defining and applying the system's objective functions - maximising the quality and width of the territory scanned.
References
[1]
McEnroe P., Wang S. and M. Liyanage, A. 2022. Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges in IEEE Internet of Things Journal, vol. 9, no. 17, 15435-15459, 1 Sept.1, 2022.
[2]
Li N., Su Z., Ling H., Karatas M. and Zheng Y. 2023. Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms, in Complex System Modeling and Simulation, vol. 3, no. 2, 102-117, June 2023.
[3]
Zhang X. and Ali M., Bean A. 2020. Optimization-Based Cooperation Method for Target Searching by Swarm UAVs in Unknown Environments, in IEEE Access, vol. 8, 43850-43862, 2020.
[4]
Gaydamaka A., Samuylov A., Moltchanov D., Ashraf M., Tan B. and Koucheryavy Y. 2023. Dynamic Topology Organization and Maintenance Algorithms for Autonomous UAV Swarms, in IEEE Transactions on Mobile Computing.
[5]
Yuen, C.H., Woo, K.T. 2017. A Survivability Enhanced Swarm Robotic Searching System Using Multi-objective Particle Swarm Optimization. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science, vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_18
[6]
Ming, R.; Jiang, R.; Luo, H.; Lai, T.; Guo, E.; Zhou, Z. 2023. Comparative Analysis of Different UAV Swarm Control Methods on Unmanned Farms. Agronomy, 13, 2499. https://doi.org/10.3390/agronomy13102499
[7]
Dai F., Chen M., Wei X. and Wang H. 2019. Swarm Intelligence-Inspired Autonomous Flocking Control in UAV Networks in IEEE Access, vol. 7, pp. 61786-61796.
[8]
Toffoli T. and Margolus N. 1987. Cellular Automata Machines, The MIT Press, ISBN 0-262-20060-0
[9]
Bilan, S. M., Bilan, M. M., and Motornyuk, R. L. 2021. New Methods and Paradigms for Modeling Dynamic Processes Based on Cellular Automata. IGI Global. https://doi.org/10.4018/978-1-7998-2649-1
[10]
Karaboga, D., Basturk, B. 2007. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39, 459–471. https://doi.org/10.1007/s10898-007-9149-x
[11]
V. Mezhuyev and E. Malakhov. 2015. Ontology-Driven Development of the Metamodels for Modelling Distributed Parallel Software Systems. 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS), Kota Kinabalu, Malaysia, 2015, pp. 389-393.
Index Terms
- Improving the survivability of a reconnaissance UAV swarm based on cellular automata
Recommendations
Cellular particle swarm optimization
This paper proposes a cellular particle swarm optimization (CPSO), hybridizing cellular automata (CA) and particle swarm optimization (PSO) for function optimization. In the proposed CPSO, a mechanism of CA is integrated in the velocity update to modify ...
An improved artificial bee colony algorithm based on the strategy of global reconnaissance
The artificial bee colony (ABC) algorithm is a recently introduced swarm intelligence optimization algorithm based on the foraging behavior of a honeybee colony. However, many problems are encountered in the ABC algorithm, such as premature convergence ...
An improved particle swarm optimisation based on cellular automata
Particle swarm optimisation PSO algorithm is easy to fall into local optimum, so an improved PSO based on cellular automata is proposed by combining cellular automata CA with PSO. In the proposed CAPSO, each particle of particle swarm is considered as ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
May 2024
324 pages
ISBN:9798400716386
DOI:10.1145/3674558
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 26 August 2024
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICCTA 2024
ICCTA 2024: 2024 10th International Conference on Computer Technology Applications
May 15 - 17, 2024
Vienna, Austria
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 113Total Downloads
- Downloads (Last 12 months)113
- Downloads (Last 6 weeks)33
Reflects downloads up to 20 Jan 2025
Other Metrics
Citations
Cited By
View allView Options
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML FormatLogin options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in