Performance analysis of hybrid optimization approach for UAV path planning control using FOPID-TID controller and HAOAROA algorithm

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 9, 2025

In this study, we present a comparative analysis of various trajectory optimization algorithms for Unmanned Aerial Vehicles (UAVs) navigating complex environments. The performance the proposed FOPID-TID based HAOAROA (Hybrid Archimedes Optimization Algorithm-Rider Algorithm) is evaluated against traditional methods such as A*, JPS, Bezier, and L-BSGF algorithms. approach integrates advantages fractional-order control with hybrid techniques to improve UAV planning. Simulation results indicate that method carries significantly better than respect length, smoothness, overall stability. Remarkably, yields 10% reduced length smoother while also being more computationally efficient. By using parameters, dynamic response becomes in challenging This shows disturbance rejection precision are much superior original two subroutines. applications presented study allow future growth system improvements provide proof concept improving UAVs dynamic,

Language: Английский

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Evaluation of a 3-DOF helicopter dynamic control model using FOPID controller-based three optimization algorithms

International Journal of Information Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 27, 2024

Language: Английский

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A new customer-oriented multi-task scheduling model for cloud manufacturing considering available periods of services using an improved hyper-heuristic algorithm

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: 269, P. 126419 - 126419

Published: Jan. 6, 2025

Language: Английский

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Evaluation of optimization algorithms for power and voltage in solar power generation system: FOPID controller and MPPT-based P&O algorithm

International Journal of Information Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 11, 2025

Language: Английский

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Multi-criteria decision model for multicircular flight control of unmanned aerial vehicles through a hybrid approach

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 30, 2025

This study presents a novel approach for optimizing UAV (unmanned aerial vehicle) Multicircular flight control by developing fractional order proportional integral derivative (FOPID)-based hybrid Eagle strategy particle swarm optimization ant lion optimizer (HESPSOALO). The proposed algorithm combines the strengths of (PSO) and (ALO), which are enhanced to systematically fine-tune FOPID controller parameters. method aims improve system stability, responsiveness, disturbance rejection in UAVs, particularly challenging dynamic conditions. was validated against traditional methods that utilize (Base), Base HESPSOALO algorithm, FOPID-based HPSOGWO (Hybrid Particle Swarm Optimization-Gray Wolf Optimizer), HGWOALO Gray Optimization-Ant Lion Optimizer) with set benchmark functions used analysis. results demonstrate minimization position angular errors, reduced oscillations, overall improved stability compared other methods. Furthermore, multicriteria decision-making (MCDM) framework is applied evaluate performance alternative strategies utilizing CRiteria importance through intercriteria correlation (CRITIC) technique preference similarity ideal solution (TOPSIS) techniques. MCDM analysis demonstrates among evaluated criteria, [Formula: see text] has highest importance, weight 0.244019, whereas deemed least significant, 0.161023. ranking reveal (Base) best-performing method, score 0.571161, indicating its superior across major metrics. In contrast, + ranks lowest, 0.282794. findings have significant industrial implications, sectors where UAVs critical precision tasks, such as logistics, agriculture, surveillance, environmental monitoring. By parameters, enhances reliability environments, resulting more precise robust under varying improvement may reduce operational risks maintenance costs while increasing efficiency, prolonging service life, achieving energy savings. provides based on potential algorithms autonomous flight.

Language: Английский

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Fuzzy analytic hierarchy process based generation management for interconnected power system

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: May 20, 2024

Decision makers consistently face the challenge of simultaneously assessing numerous attributes, determining their respective importance, and selecting an appropriate method for calculating weights. This article addresses problem automatic generation control (AGC) in a two area power system (2-APS) by proposing fuzzy analytic hierarchy process (FAHP), multi-attribute decision-making (MADM) technique, to determine weights sub-objective functions. The integral-time-absolute-errors (ITAE) tie-line fluctuation, frequency deviations errors, are defined as sub-objectives. Each these is given weight FAHP method, which then combines them into single final objective function. function used design PID controller. To improve optimization function, Jaya algorithm (JOA) conjunction with other techniques such sine cosine (SCA), Luus-Jaakola (LJA), Nelder-Mead simplex (NMSA), symbiotic organism search (SOSA) elephant herding (EHOA). Six distinct experimental cases conducted evaluate controller's performance under various load conditions, data plotted show responses corresponding fluctuations exchange. Furthermore, statistical analysis performed gain better understanding effectiveness JOA-based For non-parametric evaluation, Friedman rank test also validate proposed

Language: Английский

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Advanced Deep Learning Models for Improved IoT Network Monitoring Using Hybrid Optimization and MCDM Techniques

Symmetry, Journal Year: 2025, Volume and Issue: 17(3), P. 388 - 388

Published: March 4, 2025

This study addresses the challenge of optimizing deep learning models for IoT network monitoring, focusing on achieving a symmetrical balance between scalability and computational efficiency, which is essential real-time anomaly detection in dynamic networks. We propose two novel hybrid optimization methods—Hybrid Grey Wolf Optimization with Particle Swarm (HGWOPSO) Hybrid World Cup Harris Hawks (HWCOAHHO)—designed to symmetrically global exploration local exploitation, thereby enhancing model training adaptation environments. These methods leverage complementary search behaviors, where symmetry processes enhances convergence speed accuracy. The proposed approaches are validated using real-world datasets, demonstrating significant improvements accuracy, scalability, adaptability compared state-of-the-art techniques. Specifically, HGWOPSO combines hierarchy-driven leadership Wolves velocity updates Optimization, while HWCOAHHO synergizes strategies competition-driven algorithm, ensuring balanced decision-making processes. Performance evaluation benchmark functions data highlights superior precision, recall, F1 score traditional methods. To further enhance decision-making, Multi-Criteria Decision-Making (MCDM) framework incorporating Analytic Hierarchy Process (AHP) TOPSIS employed evaluate rank Results indicate that achieves most optimal accuracy followed closely by HGWOPSO, like FFNNs MLPs show lower effectiveness detection. symmetry-driven approach these algorithms ensures robust, adaptive, scalable monitoring solutions networks characterized traffic patterns evolving anomalies, thus stability integrity. findings have substantial implications smart cities, industrial automation, healthcare applications, performance efficiency crucial reliable monitoring. work lays groundwork research techniques learning, emphasizing role resilience systems.

Language: Английский

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Stable control of hydraulic robotic arm during operation using PLC technology

Automatika, Journal Year: 2025, Volume and Issue: 66(2), P. 300 - 305

Published: March 18, 2025

Language: Английский

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Leveraging the internet of things and optimized deep residual networks for improved foliar disease detection in apple orchards

Network Computation in Neural Systems, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 37

Published: March 24, 2025

Plant diseases significantly threaten food security by reducing the quantity and quality of agricultural products. This paper presents a deep learning approach for classifying foliar in apple plants using Tunicate Swarm Sine Cosine Algorithm-based Deep Residual Network (TSSCA-based DRN). Cluster heads simulated Internet Things (IoT) networks are selected Fractional Lion Optimization (FLION), images pre-processed with Gaussian filter segmented DeepJoint model. The TSSCA, combining Algorithm (TSA) (SCA), enhances classifier's effectiveness. Moreover, Pathology 2020 - FGVC7 dataset is used this work. designed classification trees. TSSCA-based DRN outperforms other methods, achieving 97% accuracy, 94.666% specificity, 96.888% sensitivity, 0.0442J maximal energy, significant improvements over existing approaches. Additionally, proposed model demonstrates superior outperforming methods 8.97%, 6.58%, 2.07%, 1.71%, 1.14%, 1.07%, 0.93%, 0.64% Multidimensional Feature Compensation neural network (MDFC ResNet), Convolutional Neural (CNN), Multi-Context Fusion (MCFN), Advanced Segmented Dimension Extraction (ASDE), DRN, fuzzy convolutional (FCDCNN), ResNet9-SE, Capsule (CapsNet), IoT-based scrutinizing model, Multi-Model (MMF-Net).

Language: Английский

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Decidable Regression Techniques for Statistical Modelling with Sustainable Agriculture Operations

SN Computer Science, Journal Year: 2024, Volume and Issue: 5(8)

Published: Dec. 11, 2024

Language: Английский

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