Enhanced Artificial Gorilla Troops Optimizer Based Clustering Protocol for UAV-Assisted Intelligent Vehicular Network

Alsolai, Hadeel; Maray, Mohammed; Alghamdi, Mohammed G.; Qahmash, Ayman; Alnfiai, Mrim M.; Aziz, Amira Sayed A.; Hilal, Anwer Mustafa

doi:10.3390/drones6110358

Cited by 12 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the AGTO algorithm, five different operators are used to simulate the collective behavior of gorillas, mainly divided into two stages: exploration stage and development stage. The exploration phase employed three different mechanisms, namely migration to unknown locations, migration to other gorilla locations, and migration to known locations 37 . During the development phase, two social behaviors were adopted: following the silver backed gorilla and competing with adult female gorillas.…”

Section: Artificial Gorilla Troops Optimizer Algorithmmentioning

confidence: 99%

Gorilla optimization algorithm combining sine cosine and cauchy variations and its engineering applications

Wang,

Cao,

Chen

et al. 2024

Sci Rep

View full text Add to dashboard Cite

To address the issues of lacking ability, loss of population diversity, and tendency to fall into the local extreme value in the later stage of optimization searching, resulting in slow convergence and lack of exploration ability of the artificial gorilla troops optimizer algorithm (AGTO), this paper proposes a gorilla search algorithm that integrates the positive cosine and Cauchy's variance (SCAGTO). Firstly, the population is initialized using the refractive reverse learning mechanism to increase species diversity. A positive cosine strategy and nonlinearly decreasing search and weight factors are introduced into the finder position update to coordinate the global and local optimization ability of the algorithm. The follower position is updated by introducing Cauchy variation to perturb the optimal solution, thereby improving the algorithm's ability to obtain the global optimal solution. The SCAGTO algorithm is evaluated using 30 classical test functions of Test Functions 2018 in terms of convergence speed, convergence accuracy, average absolute error, and other indexes, and two engineering design optimization problems, namely, the pressure vessel optimization design problem and the welded beam design problem, are introduced for verification. The experimental results demonstrate that the improved gorilla search algorithm significantly enhances convergence speed and optimization accuracy, and exhibits good robustness. The SCAGTO algorithm demonstrates certain solution advantages in optimizing the pressure vessel design problem and welded beam design problem, verifying the superior optimization ability and engineering practicality of the SCAGTO algorithm.

show abstract

Section: Artificial Gorilla Troops Optimizer Algorithmmentioning

confidence: 99%

Gorilla optimization algorithm combining sine cosine and cauchy variations and its engineering applications

Wang,

Cao,

Chen

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The WSOODL-UAVCSC method measures a fitness function by adding various parameters. The WSOODL-UAVCSC technique is developed with the existence of four fitness parameters such as UAV nodes, average distance of UAVs for CHs enclosed by the sensing range, distance in CH to sink, and energy efficiency of cluster node density 45 . The data on fitness parameter was shown as follows: Energy efficiency: The CH performs diverse activities namely sense, gathered, aggregation, data broadcast, etc.…”

Section: Process Involved In Clustering Techniquementioning

confidence: 99%

White shark optimizer with optimal deep learning based effective unmanned aerial vehicles communication and scene classification

Nadana Ravishankar,

Ramprasath,

Daniel

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs) become a promising enabler for the next generation of wireless networks with the tremendous growth in electronics and communications. The application of UAV communications comprises messages relying on coverage extension for transmission networks after disasters, Internet of Things (IoT) devices, and dispatching distress messages from the device positioned within the coverage hole to the emergency centre. But there are some problems in enhancing UAV clustering and scene classification using deep learning approaches for enhancing performance. This article presents a new White Shark Optimizer with Optimal Deep Learning based Effective Unmanned Aerial Vehicles Communication and Scene Classification (WSOODL-UAVCSC) technique. UAV clustering and scene categorization present many deep learning challenges in disaster management: scene understanding complexity, data variability and abundance, visual data feature extraction, nonlinear and high-dimensional data, adaptability and generalization, real-time decision making, UAV clustering optimization, sparse and incomplete data. the need to handle complex, high-dimensional data, adapt to changing environments, and make quick, correct decisions in critical situations drives deep learning in UAV clustering and scene categorization. The purpose of the WSOODL-UAVCSC technique is to cluster the UAVs for effective communication and scene classification. The WSO algorithm is utilized for the optimization of the UAV clustering process and enables to accomplish effective communication and interaction in the network. With dynamic adjustment of the clustering, the WSO algorithm improves the performance and robustness of the UAV system. For the scene classification process, the WSOODL-UAVCSC technique involves capsule network (CapsNet) feature extraction, marine predators algorithm (MPA) based hyperparameter tuning, and echo state network (ESN) classification. A wide-ranging simulation analysis was conducted to validate the enriched performance of the WSOODL-UAVCSC approach. Extensive result analysis pointed out the enhanced performance of the WSOODL-UAVCSC method over other existing techniques. The WSOODL-UAVCSC method achieved an accuracy of 99.12%, precision of 97.45%, recall of 98.90%, and F1-score of 98.10% when compared to other existing techniques.

show abstract

“…During the 1960 to 1970 Vietnam War, the need to respond with missile attacks, so drone decoys were often used to deceive radar, such as the McConnellADM-20 Quail. After the end of the Vietnam War in 1975, Engineers have developed many new drone models, the [5,6].…”

Section: Introductionmentioning

confidence: 99%

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

Song

2024

JCSANDM

View full text Add to dashboard Cite

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.

show abstract

Enhanced Artificial Gorilla Troops Optimizer Based Clustering Protocol for UAV-Assisted Intelligent Vehicular Network

Cited by 12 publications

References 21 publications

Gorilla optimization algorithm combining sine cosine and cauchy variations and its engineering applications

Gorilla optimization algorithm combining sine cosine and cauchy variations and its engineering applications

White shark optimizer with optimal deep learning based effective unmanned aerial vehicles communication and scene classification

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

Contact Info

Product

Resources

About