Bi-objective Optimization for Collaborative UAV Secure Communication under Forest Channel

Li, Ruonan; Wang, Aimin; Sun, Geng; Li, Jiahui; Zheng, Xiaoya

doi:10.1109/iscc55528.2022.9913031

Cited by 2 publications

(1 citation statement)

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“…Consequently, the swarm algorithm harnesses chaotic variables' properties, facilitating offspring individuals' traversal across solution spaces. This strategic modification minimizes the inherent randomness and myopia of search processes, enhancing both efficiency and efficacy in identifying optimal solutions, as referenced in [25,26]. The inception of the artificial swarm algorithm can be attributed to Kara Boga in 2005, subsequently garnering considerable scholarly attention.…”

Section: Search Strategy Improvementmentioning

confidence: 99%

Analysis and Application of Chaotic Genetic Algorithm Based on Network Security in The Research of Resilience of Cluster Networks

Song

2024

JCSANDM

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With the wide application of UAV, in the actual flight process, UAV needs to calculate the safe path according to its own position, environment, obstacles and other information. Due to the complex and changeable scene and environment of UAV mission execution, it is very important to select an appropriate UAV path planning algorithm. This paper aims at the path planning problem of multiple UAVs in a complex three-dimensional environment to ensure that multiple UAVs reach the mission location from different angles. Taking the chaotic genetic algorithm in network security protection as the main body, the operation difficulty of the algorithm is reduced, and the solution speed and accuracy of the algorithm are improved. The path length obtained by the proposed algorithm is 8.4% less than that of the ABC algorithm, 11.3% less than that of the PSO algorithm, and 4.2% less than that of the BABC algorithm. The system running time of the improved algorithm is also reduced by 27% to 45% compared with other algorithms. In terms of unmanned cooperation, this paper proposes a system capability based on network modeling to improve the cooperative combat capability of multiple UAVs. By establishing a network model, information sharing, collaborative decision-making and collaborative decision-making between drones are realized, thereby improving the effectiveness of the entire system. At the same time, this paper also considers the problem of network survivability. By introducing redundant design and fault recovery mechanism, the robustness and reliability of the system are enhanced.

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Section: Search Strategy Improvementmentioning

confidence: 99%