Search Results (117)

The growing demand for reliable and secure vehicle-to-vehicle (V2V) communication in next-generation intelligent transportation systems has accelerated the adoption of reconfigurable intelligent surfaces (RIS) as a means of enhancing link quality, spectral efficiency, and physical layer security. In this paper, we investigate the problem of secrecy rate maximization in a cooperative dual-RIS-aided V2V communication network, where two cascaded RISs are deployed to collaboratively assist with secure data transmission between mobile vehicular nodes in the presence of eavesdroppers. To address the inherent complexity of time-varying wireless channels, we propose a novel evolutionary transformer-gated recurrent unit (Evo-Transformer-GRU) framework that jointly learns temporal channel patterns and optimizes the RIS reflection coefficients, beam-forming vectors, and cooperative communication strategies. Our model integrates the sequence modeling strength of GRUs with the global attention mechanism of transformer encoders, enabling the efficient representation of time-series channel behavior and long-range dependencies. To further enhance convergence and secrecy performance, we incorporate an improved gray wolf optimizer (IGWO) to adaptively regulate the model’s hyper-parameters and fine-tune the RIS phase shifts, resulting in a more stable and optimized learning process. Extensive simulations demonstrate the superiority of the proposed framework compared to existing baselines, such as transformer, bidirectional encoder representations from transformers (BERT), deep reinforcement learning (DRL), long short-term memory (LSTM), and GRU models. Specifically, our method achieves an up to 32.6% improvement in average secrecy rate and a 28.4% lower convergence time under varying channel conditions and eavesdropper locations. In addition to secrecy rate improvements, the proposed model achieved a root mean square error (RMSE) of 0.05, coefficient of determination ($R^2$) score of 0.96, and mean absolute percentage error (MAPE) of just 0.73%, outperforming all baseline methods in prediction accuracy and robustness. Furthermore, Evo-Transformer-GRU demonstrated rapid convergence within 100 epochs, the lowest variance across multiple runs. Full article

To comprehensively address the challenges of aperture design, element spacing optimization, and sidelobe suppression in sparse radar array antennas, this paper proposes a hybrid particle swarm optimization (PSO) algorithm that integrates quantum-behavior mechanisms with genetic mutation. The algorithm enhances global search capability through the introduction of a quantum potential well model, while incorporating adaptive mutation operations to prevent premature convergence, thereby improving optimization accuracy during later iterations. The simulation results demonstrate that for sparse linear arrays, planar rectangular arrays, and multi-ring concentric circular arrays, the proposed algorithm achieves a sidelobe level (SLL) reduction exceeding 0.24 dB compared to conventional approaches, including the grey wolf optimizer (GWO), the whale optimization algorithm (WOA), and classical PSO. Furthermore, it exhibits superior global iterative search performance and demonstrates broader applicability across various array configurations. Full article

The Dual Active Bridge converter (DABC), known for its bidirectional power transfer capability and high efficiency, plays a crucial role in various applications, particularly in electric vehicles (EVs), where it facilitates energy storage, battery charging, and grid integration. The Dual Active Bridge Converter (DABC), when paired with a high-performance CLLC filter, is well-regarded for its ability to transfer power bidirectionally with high efficiency, making it valuable across a range of energy applications. While these features make the DABC highly efficient, they also complicate controller design due to nonlinear behavior, fast switching, and sensitivity to component variations. We have used a Fractional-order PID (FOPID) controller to benefit from the simple structure of classical PID controllers with lower complexity and improved flexibility because of additional filtering gains adopted in this method. However, for a FOPID controller to operate effectively under real-time conditions, its parameters must adapt continuously to changes in the system. To achieve this adaptability, a Multi-Agent Reinforcement Learning (MARL) approach is adopted, where each gain of the controller is tuned individually using the Deep Deterministic Policy Gradient (DDPG) algorithm. This structure enhances the controller’s ability to respond to external disturbances with greater robustness and adaptability. Meanwhile, finding the best initial gains in the RL structure can decrease the overall efficiency and tracking performance of the controller. To overcome this issue, Grey Wolf Optimization (GWO) algorithm is proposed to identify the most suitable initial gains for each agent, providing faster adaptation and consistent performance during the training process. The complete approach is tested using a Hardware-in-the-Loop (HIL) platform, where results confirm accurate voltage control and resilient dynamic behavior under practical conditions. In addition, the controller’s performance was validated under a battery management scenario where the DAB converter interacts with a nonlinear lithium-ion battery. The controller successfully regulated the State of Charge (SOC) through automated charging and discharging transitions, demonstrating its real-time adaptability for BMS-integrated EV systems. Consequently, the proposed MARL-FOPID controller reported better disturbance-rejection performance in different working cases compared to other conventional methods. Full article

The integration of intermittent Distributed Generations (DGs) like solar photovoltaics into Radial Distribution Systems (RDSs) reduces system losses but causes voltage and power instability issues. It has also been observed that seasonal variations affect the performance of such DGs. These issues can be resolved by placing optimum-sized Battery Energy Storage (BES) Systems into RDSs. This work proposes a new approach to the placement of optimally sized BESSs considering multiple objectives, Active Power Losses, the Power Stability Index, and the Voltage Stability Index, which are prioritized using the Weighted Sum Method. The proposed multi-objectives are investigated using the probabilistic and Polynomial Multiple Regression (PMR) approaches to account for the randomness in solar irradiance and its effect on BESS sizing and placements. To analyze system behavior, simultaneous and sequential strategies considering aggregated and distributed BESS placement are executed on IEEE 33-bus and 94-bus Portuguese RDSs by applying the Improved Grey Wolf Optimization and TOPSIS techniques. Significant loss reduction is observed in distributed BESS placement compared to aggregated BESSs. Also, the sequentially distributed BESS stabilized the RDS to a greater extent than the simultaneously distributed BESS. In view of the uncertainty, the probabilistic and PMR approaches require a larger optimal BESS size than the deterministic approach, representing practical systems. Additionally, the results are validated using Improved Particle Swarm Optimization–TOPSIS techniques. Full article

Meta-heuristic algorithms are computational methods inspired by evolutionary processes, animal or plant behaviors, physical events, and other natural phenomena. Due to their success in solving optimization problems, meta-heuristic algorithms are widely used in the literature, leading to the development of novel variants. In this paper, new swarm-based meta-heuristic algorithms, called Improved Roosters Algorithm (IRA), Bipolar Roosters Algorithm (BRA), and Bipolar Improved Roosters Algorithm (BIRA), which are mainly based on Roosters Algorithm (RA), are presented. First, the new versions of RA (IRA, BRA, and BIRA) were compared in terms of performance, revealing that BIRA achieved significantly better results than the other variants. Then, the performance of the BIRA algorithm was compared with the performances of meta-heuristic algorithms widely used in the literature, Standard Genetic Algorithm (SGA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Cuckoo Search (CS), and Grey Wolf Optimizer (GWO), and thus, its success in the literature was tested. Moreover, RA was also included in this test to show that the new version, BIRA, is more successful than the previous one (RA). For all comparisons, 20 well-known benchmark optimization functions, 11 CEC2014 test functions, and 17 CEC2018 test functions, which are also in the CEC2020 test suite, were employed. To validate the significance of the results, Friedman and Wilcoxon Signed Rank statistical tests were conducted. In addition, three commonly used problems in the field of engineering were used to test the success of algorithms in real-life scenarios: pressure vessel, gear train, and tension/compression spring design. The results indicate that the proposed algorithm (BIRA) provides better performance compared to the other meta-heuristic algorithms. Full article

Piezoelectric tilt mirrors are crucial components of precision optical systems. However, the intrinsic hysteretic nonlinearity of the piezoelectric actuator severely restricts the control accuracy of these mirrors and the overall performance of the optical system. This paper proposes an improved Gray Wolf Optimization (GWO) algorithm for high-accuracy identification of hysteresis model parameters based on the Bouc–Wen (BW) differential equation. The proposed algorithm accurately describes the intrinsic hysteretic nonlinear behavior of piezoelectric tilt mirrors. A logistic chaotic mapping method is introduced for population initialization, while a nonlinear convergence factor and a Levy flight strategy are incorporated to enhance global search capabilities during the later stages of optimization. These modifications enable the algorithm to effectively identify BW model parameters for piezoelectric nonlinear systems. Compared to conventional Particle Swarm Optimization (PSO) and standard GWO, the improved algorithm demonstrates faster convergence, higher accuracy, and superior ergodicity, making it a promising tool for solving optimization problems, such as parameter identification in piezoelectric hysteresis systems. This work provides a robust approach for improving the precision and reliability of piezoelectric-driven optical systems. Full article

The control of nonlinear systems cannot be effectively achieved using linear mathematical methods. This paper introduces a novel mathematical model to characterize the voltage–current (V–I) characteristics of the electric arc furnace (EAF) melting process, incorporating experimental field data for validation. The proposed model integrates polynomial curve fitting, the modified Heidler function, and double S-curves, with the grey wolf optimization (GWO) algorithm applied for parameter optimization, enhancing accuracy in predicting arc dynamics. The performance of the model is compared against the exponential, hyperbolic, exponential–hyperbolic, and nonlinear resistance models, as well as real-time measurement data, to assess its effectiveness. The results show that the proposed model significantly reduces voltage and current harmonic distortion compared to existing models. Specifically, the total harmonic distortion (THD) for voltage is reduced to 2.34%, closely matching the real-time measured value of 2.30%. Similarly, in the current spectrum, the proposed model achieves a significant reduction in third harmonic distortion and a THD of 11.40%, compared to 13.76% in real-time measurements. Consequently, a more precise characterization of the EAF behavior enables more effective mitigation of harmonics and vibrations, enhancing the stability and power quality of the electrical networks to which they are connected. Full article

Wildlife behavior can be influenced by the deployment of sensory cues in a landscape, but different cues vary in the strength and duration of their effectiveness. We aimed to identify the most effective and cost-efficient countermeasures (sensory cues) to deter wild boar (Sus scrofa) entry and damage to cornfields in Hunchun, Jilin Province, China. These cornfields have experienced severe damage by wild boars during the critical 30-day period when this crop was ripening. From 2016 to 2021, different countermeasures were applied sequentially seeking to control this damage by using either (1) visual deterrents, i.e., solar blinkers of different colors; (2) auditory deterrents, i.e., playbacks of Amur tiger (Panthera tigris altaica) calls, wild boar calls, or wolf (Canis lupus) calls; (3) tactile deterrents, i.e., electric fencing; (4) olfactory deterrents, i.e., Adult Amur tiger feces; or (5) various combined deterrents. We first evaluated the effectiveness of these broad categories, then performed a detailed analysis of the individual countermeasures to assess their specific deterrence effectiveness and duration. A cost-effective analysis was subsequently performed on the most effective countermeasures to evaluate the best option for practical applications. Across the broad categories of deterrents, the tactile group proved the most effective overall. For individual deterrents, the seven countermeasures showing significantly higher effectiveness than the others tested included the following: (1) 1000 mA red solar blinker (32.25 ± 4.22 days), (2) 1000 mA yellow solar blinker (29.67 ± 4.58 days), (3) 1000 mA green solar blinker (29.58 ± 5.60 days), (4) electric fencing with three wires (29.67 ± 0.58 days), (5) electric fencing with two wires (28.00 ± 2.00 days), (6) Adult Amur tiger calls for 15 s and wild boar calls for 15 s plus a combined 30 s plus a blank recording for 5 min (26.50 ± 2.38 days), and (7) Adult Amur tiger feces and calls (27.34 ± 2.94 days). Except for the Adult Amur tiger feces and calls, each countermeasure would cover most of the period over which control is necessary (30 days). The 1000 mA red solar blinker of achieved the highest repellency per cost ratio (0.31) at 30.29 IUS$/hm² but showed reduced effectiveness over time. Although electric fencing with three wires offers longer deterrence, its cost-effectiveness ratio was lower (0.27) due to higher installation and maintenance costs at 319.69 IUS$/hm². The 1000 mA red solar blinker offers a highly cost-effective short-term deterrent, while the electric fencing with three wires provides durable, long-term protection despite its higher costs. Balancing cost and duration can optimize wild boar deterrence strategies across different management needs. Full article

The rapid growth of electric vehicle (EV) adoption presents significant challenges in planning efficient charging infrastructure, including suboptimal station placement, energy consumption, and rising infrastructural costs. The conventional methods, such as grey wolf optimization (GWO), fail to address real-time user demand and dynamic factors like fluctuating grid loads and environmental impact. These approaches rely on fixed models, often leading to inefficient energy use, higher operational costs, and increased traffic congestion. This paper proposes a novel framework that integrates deep Q networks (DQNs) for real-time charging optimization, coupled with multimodal bioinspired algorithms like ant lion optimization (ALO) and moth flame optimization (MFO). Unlike conventional geographic placement models that overlook evolving travel patterns, this system dynamically adapts to user behavior, optimizing both onboard and offboard charging systems. The DQN enables continuous learning from changing demand and grid conditions, while ALO and MFO identify optimal station locations, reducing energy consumption and emissions. The proposed framework incorporates dynamic pricing and demand response strategies. These adjustments help balance energy usage, reducing costs and preventing overloading of the grid during peak times, offering real-time adaptability, optimized station placement, and energy efficiency. To improve the performance of the system, the proposed framework ensures more sustainable, cost-effective EV infrastructural planning, minimized environmental impacts, and enhanced charging efficiency. From the results for the proposed system, we recorded various performance parameters such as the installation cost, which decreased to USD 1200 per unit, i.e., a 20% cost efficiency increase, optimal energy utilization increases to 85% and 92% during peak hours and off-peak hours respectively, a charging slot availability increase to 95%, a 30% carbon emission reduction, and 95% performance retention under the stress condition. Further, the power quality is improved by reducing the sag, swell, flicker, and notch by 2 V, 3 V, 0.05 V, and 0.03 V, respectively, with an increase in efficiency to 89.9%. This study addresses critical gaps in real-time flexibility, cost-effective station deployment, and grid resilience by offering a scalable and intelligent EV charging solution. Full article

The study of carnivores’ diets is an important conservation tool, which can minimize conflicts with different stakeholders and provide proper substantiations for management measures. The main aim of this study was to understand the diet of a small, isolated pack named Mogadouro Sul, present in the Northeast of Trás-os-Montes (Portugal). Scat analysis was the method used to determine what wolves consumed. The specific origin of all collected fecal samples (n = 78) was confirmed by molecular analysis. The results, expressed in frequency of occurrence (FO) showed that domestic animals were the most frequent food category in the wolf’s diet (78.3% FO), with a special incidence in goats (40.6% FO), although wild ungulates (roe deer and wild boar) also accounted for 21.7% FO of the diet. The study pack presented a diet diversity (H′) of 0.65 and a food niche breadth (B′) of 0.55. Food availability did not appear to be a limiting factor, and the wolf’s presence in the study area could be explained by changes in land use and increased infrastructure. This trophic behavior may threaten the conservation of this pack due to the persecution it may face. Full article

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

Human–wildlife conflict poses significant ecological and socio-economic challenges, particularly in rural communities where agriculture and livestock rearing form the backbone of livelihoods. Despite the growing importance of this issue, District Lakki Marwat remains an unexplored area of northwest Pakistan. This study aims to fill this gap by systematically assessing the status, economic impacts, and community perceptions of five wildlife species: wild boar (Sus scrofa), grey wolf (Canis lupus), golden jackal (Canis aureus), striped hyena (Hyaena hyaena), and red fox (Vulpes vulpes). Using semi-structured surveys with 117 respondents, we analyzed species prevalence, perceived danger levels, crop damage patterns, and predation impacts on livestock and poultry. The findings revealed that wild boars were identified as the primary contributors to agricultural damage, with total annual crop losses surpassing the economic impacts attributed to the studied carnivores. On average, each surveyed household experienced an annual loss of PKR 4510.38. For the 39% of households reporting crop damage, the annual loss per reported household was PKR 11,727, which was higher than the average annual loss across all households, underscoring the severity of the impact on those specifically affected by the wild boar-related crop damage. Notably, community attitudes were most negative toward wild boars, a pattern driven by the economic burden of crop losses, challenging the conventional focus on carnivores as the primary conflict species. A Pearson’s $X^2$ test confirmed strong associations between species and perceived danger levels, while regression analysis demonstrated an association between crop damage and negative attitudes. Traditional deterrents like thorn fences were found ineffective against wild boars. More advanced methods, including game-proof fencing, trenches, bio-fencing, crop rotation, audio and visual deterrents, taste and order repellents, and watchtowers combined with group vigilance, are recommended to reduce crop damage. Integrating these approaches with community-based education, habitat management, and government-supported compensation schemes can mitigate wild boar impacts. This study contributes new insights into multi-species HWC dynamics, demonstrating that community perceptions are primarily shaped by the economic impact of a species, regardless of whether it is a carnivore or an omnivore. The attitudes of local communities are driven by the financial losses incurred, rather than the species' behavior or ecological role. This study underscores the need for collaborative efforts to reduce human–wildlife conflict, foster coexistence, and ensure ecological balance in vulnerable rural areas. Full article

In human-dominated landscapes, human activities shape prey spatial behavior, creating complex landscapes of risks. We investigated habitat selection of roe deer using resource selection functions in a human-dominated mountain system located in the southwestern Alps, characterized by a high presence of wolves and human disturbance. Our study aimed to assess how the interplay of hunting, presence of infrastructures, and recreational activities in the presence of wolves influenced roe deer spatial responses inside and outside a protected area. We documented that during the hunting period, roe deer increased selection of high-wolf-density areas, with the strongest effect observed during wild boar drive hunts, supporting the risk enhancement hypothesis, where avoiding one predator increases exposure to another, and highlighting the temporary yet significant impact of hunting on predator–prey dynamics. During the period of the wild boar drive hunt, roe deer also showed stronger selection for proximity to buildings, supporting the human shield hypothesis. Protected areas had an increased effect on roe deer avoidance of trails, where hiking and recreational activities are more concentrated. Our findings revealed the complex trade-offs that roe deer face in navigating multiple risks within human-modified landscapes, important for the development of effective conservation and human sustainability strategies. Full article

The grey wolf optimization (GWO) algorithm is a simple and effective meta-heuristic algorithm that mimics the leadership of grey wolves and the social behavior of wolves in nature. However, the updating of GWO population positions only relies on the guidance of $\alpha$-wolf, $\beta$-wolf, and $\delta$-wolf, and individuals are updated with equal weights. This results in the GWO search process being unable to utilize the knowledge of superior wolves better. Therefore, in this study, we propose for the first time an attention mechanism-based GWO (AtGWO). Firstly, when each position is updated, the attention strategy can adaptively assign the weight of the corresponding leader wolf to improve the global exploration ability. Second, with the introduction of omega-wolves, each position update is not only guided by the three leader wolves but also learns from their current optimal values. Finally, a hyperbolic tangent nonlinear function is used to control the convergence factor to better balance exploration and exploitation. To validate its effectiveness, AtGWO is compared with the latest GWO variant with other popular algorithms on the CEC-2014 (dim 30, 50) and CEC-2017 (dim 30, 50, 100) benchmark function sets. The experimental results indicate that AtGWO outperforms the GWO-related variants almost all the time in terms of mean, variance, and best value, which indicates its superior ability and robustness to find optimal solutions. And it is also competitive when compared to other algorithms in multimodal functions. AtGWO outperforms the comparison algorithms in terms of the mean and best value in six real-world engineering optimization problems. Full article

The significance of the Hybrid Renewable Energy System (HRES) is profound in the current scenario owing to the mounting energy requirements, pressing ecological concerns and the pursuit of transitioning to greener energy alternatives. Thereby, the modeling and design of HRES, encompassing PV–WECS–Battery, which mainly focuses on efficient power conversion and advanced control strategy, is proposed. The voltage gain of the PV system is improved using the Re-lift Luo converter, which offers high efficiency and power density with minimized ripples and power losses. Its voltage lift technique mitigates parasitic effects and delivers improved output voltage for grid synchronization. To control and stabilize the converter output, a Proportional–Integral (PI) controller tuned using a novel hybrid algorithm combining Grey Wolf Optimization (GWO) with Hermit Crab Optimization (HCO) is implemented. GWO follows the hunting and leadership characteristics of grey wolves for improved simplicity and robustness. By simulating the shell selection behavior of hermit crabs, the HCO adds diversity to exploitation. Due to these features, the hybrid GWO–HCO algorithm enhances the PI controller’s capability of handling dynamic non-linear systems, generating better control accuracy, and rapid convergence to optimal solutions. Considering the Wind Energy Conversion System (WECS), the PI controller assures improved stability despite fluctuations in wind. A Recurrent Neural Network (RNN)-based battery management system is also incorporated for accurate monitoring and control of the State of Charge (SoC) and the terminal voltage of battery storage. The simulation is conducted in MATLAB Simulink 2021a, and a lab-scale prototype is implemented for real-time validation. The Re-lift Luo converter achieves an efficiency of 97.5% and a voltage gain of 1:10 with reduced oscillations and faster settling time using a Hybrid GWO–HCO–PI controller. Moreover, the THD is reduced to 1.16%, which indicates high power quality and reduced harmonics. Full article