Wolf Research Articles

Monitoring of pathogenic Leptospira infection in wolves (Canis lupus) from Spain and Italy

Leptospirosis is a bacterial disease of worldwide distribution with relevant implications for animal and human health. Different large wild carnivore species can act as reservoirs of this zoonotic pathogen. This study aimed to evaluate the circulation of Leptospira spp. in free-ranging wolves (Canis lupus) from southern Europe. A total of 281 kidney samples of wolves from Spain and Italy were collected between 2017 and 2023. The presence of Leptospira DNA was analysed by real-time PCR and phylogenetic analyses were carried out using a Bayesian approach. The overall prevalence was 3.2 % (9/281; 95 %CI: 1.1–5.3). Leptospira DNA was detected in nine of the 180 wolves from Spain (5.0 %; 95 %CI: 1.8–8.2), but not in the Italian wolf population (0 %; 0/101). Molecular analyses revealed high homology between the sequences obtained in the present study and isolates of Leptospira interrogans and Leptospira borgpetersenii from different rodent and domestic ungulate species. Our results provide evidence of a low and spatially heterogeneous circulation of this pathogen in wolf populations of southern Europe. The detection of zoonotic Leptospira species in this survey supports the need to consider wolf populations in monitoring programs for leptospirosis with a One Health approach.

FOMCON Toolbox-Based Direct Approximation of Fractional Order Systems Using Gaze Cues Learning-Based Grey Wolf Optimizer

This study discusses a new method for the fractional-order system reduction. It offers an adaptable framework for approximating various fractional-order systems (FOSs), including commensurate and non-commensurate. The fractional-order modeling and control (FOMCON) toolbox in MATLAB and the gaze cues learning-based grey wolf optimizer (GGWO) technique form the basis of the recommended method. The fundamental advantage of the offered method is that it does not need intermediate steps, a mathematical substitution, or an operator-based approximation for the order reduction of a commensurate and non-commensurate FOS. The cost function is set up so that the sum of the integral squared differences in step responses and the root mean squared differences in Bode magnitude plots between the original FOS and the reduced models is as tiny as possible. Two case studies support the suggested method. The simulation results show that the reduced approximations constructed using the methodology under consideration have step and Bode responses more in line with the actual FOS. The effectiveness of the advocated strategy is further shown by contrasting several performance metrics with some of the contemporary approaches disseminated in academic journals.

Forecasting the output of high-tech industry in China: A novel nonlinear grey time-delay multivariable model with variable lag parameters

Under the rapidly developing economy in China, accurate forecasting holds vital significance for policymaking and operational planning within the high-tech industry. However, the influencing factors affecting the output, accompanied by the time-delay effect, could be nonlinear, and uncertain. Thereby, this paper proposes a new nonlinear grey multivariable model with time-varying lag parameters. To be specific, the newly designed time delay function and power exponent are introduced, which can significantly enhance the adaptability and flexibility of the proposed method. The Grey Wolf Optimization algorithm is utilized to calculate the dynamic time lag parameters and power exponent to improve the prediction reliability. Furthermore, this new approach is applied to predict the high-tech industry’s output in China, Shanghai Municipality, and the Eastern Region, with due consideration given to the time-delay effect between input factors and outputs. To assess its efficacy, some leading models are selected for comparison to the proposed model. Furthermore, the utilization of Monte-Carlo simulation, the Probability Density Analysis, and the simulations are used to demonstrate the robustness and stability of this new method. The findings show that the proposed model is a feasible and applicable approach for prediction, exhibiting outstanding accuracy.

Multi-energy load forecasting for integrated energy system based on sequence decomposition fusion and factors correlation analysis

Considering the seasonal and cyclical fluctuation of loads and the complexity of multi-energy coupling, this paper proposes a novel load forecasting model based on sequence decomposition fusion and factors correlation analysis. Firstly, the variational mode decomposition (VMD) is used to decompose the highly complex load sequences and the novel influencing factors correlation analysis (ICA) is proposed to select strong factors and remove weak feature variables to construct the input and output set. Secondly, this paper proposes the combined forecasting framework MTL-CNN-BiGRU-Attention to simultaneously forecast the cooling, heat, and electricity loads, along with BiGRU used as the hard sharing layer to deeply explore the coupling information between different types of loads. Meanwhile, the gray wolf algorithm (GWO) is improved to accurately and quickly search for the optimal hyperparameters of the model. Finally, the dataset of a comprehensive energy station in Shanghai is used to test our model, and the results show that the MAPE of the cooling and electricity loads forecasting achieve 5.501% and 5.821% in summer and 5.921%, 7.899% and 7.541% for the cooling, heat and electricity loads in transition season and winter, which confirms the effectiveness and superiority of our model.

Cognitive and Intellectual Behavior of the Wolf (Canis lupus L.): Barriers Preventing the Breeding of a Population of Elk (Alces alces L.) by a Predator

Cognitive and Intellectual Behavior of the Wolf (Canis lupus L.): Barriers Preventing the Breeding of a Population of Elk (Alces alces L.) by a Predator

Synthesis of sparse rectangular planar arrays with weight function and improved grey wolf optimization algorithm

AbstractThe present article proposes a novel hybrid approach for addressing the synthesis problem of rectangular planar arrays under multiple constraints, through joint optimization of the weight function and the improved grey wolf optimization (IGWO) algorithm. Firstly, the grey wolf optimization (GWO) algorithm is improved by using tent chaotic mapping, nonlinear convergence factor, dominant wolf dynamic belief strategy, and opposition‐based learning strategy to increase the population diversity and the ability to jump out of the local optimum. Secondly, the array elements are weighted using the weight function to generate the position distribution matrix, which reduces the thinned matrix optimization time and improves the optimization efficiency. Finally, the position distribution matrix is used to generate the thinned array, and the IGWO algorithm is applied to perform the sparse optimization with multiple constraints. The effectiveness of the method is verified through numerical simulation and full‐wave simulation experiments, demonstrating its capability to enhance array antenna performance and reduce peak sidelobe level (PSLL). These experimental results hold significant engineering implications and provide valuable references for addressing the array distribution problem under multiple constraints.

Metaheuristic algorithms for capacitated controller placement in software defined networks considering failure resilience

SummarySoftware‐defined networking (SDN) has revolutionized network architectures by decoupling the control plane from the data plane. An intriguing challenge within this paradigm is the strategic placement of controllers and the allocation of switches to optimize network performance and resilience. In the event of a controller failure, the switches are disconnected from the controller until they are reassigned to other active controllers possessing sufficient spare capacity. The reassignment could lead to a significant rise in propagation latency. This correspondence presents a mathematical model for capacitated controller placement, strategically designed to anticipate failures and prevent a substantial increase in worst‐case latency and disconnections. The aim is to minimize the worst‐case latency between switches and their backup controllers and among the controllers. Four metaheuristic algorithms are proposed including, an enhanced genetic algorithm (CCPCFR‐EGA), particle swarm optimization (CCPCFR‐PSO), a hybrid particle swarm optimization and simulated annealing algorithm (CCPCFR‐HPSOSA), and a grey wolf optimization algorithm (CCPCFR‐GWO). These algorithms are compared with a simulated annealing method and an optimal method. Evaluation conducted on four network datasets demonstrates that the proposed metaheuristic methods are faster than the optimal method. The experimental outcome indicates that CCPCFR‐HPSOSA and CCPCFR‐GWO outperform the other methods, consistently providing near‐optimal solutions. However, CCPCFR‐GWO is preferred over CCPCFR‐HPSOSA due to its faster execution time. Specifically, CCPCFR‐GWO achieves an average speed‐up of 3.9 over the optimal for smaller networks and an average speed‐up of 31.78 for larger networks, while still producing near‐optimal solutions.

Enhancing blasting efficiency: A smart predictive model for cost optimization and risk reduction

Mineral extraction involves distinct stages, including drilling, blasting, loading, transporting, and processing minerals at a designated facility. The initial phase is drilling and blasting, crucial for controlled dimensions of crushed stone suitable for the processing plant. Incorrect blasting can lead to unsuitable stone grading and destructive outcomes like ground vibrations, stone projection, air blasts, and recoil. Predicting and optimizing blasting costs (BC) is essential to achieve desired particle size reduction while mitigating adverse blasting consequences. BC varies with rock hardness, blasting techniques, and patterns. This study presents a BC prediction model using data from 6 Iranian limestone mines, employing firefly (FF) and gray wolf optimization (GWO) algorithms. With 146 data points and parameters like hole diameter (D), ANFO (AN), sub-drilling (J), uniaxial compressive strength (σc), burden (B), hole number (N), umolite (EM),spacing (S), specific gravity (γr), stemming (T), hole length (H), rock hardness (HA), and electric detonators (Det), the data was split into 80% for model construction and 20% for validation. Using statistical indicators, the model showed good performance, offering engineers, researchers, and mining professionals high accuracy. The @RISK software conducted sensitivity analysis, revealing T parameter as the most influential input factor, where minor T changes significantly affected BC. Lastly, the @RISK software was employed to conduct a sensitivity analysis on the model's outputs. The analyses demonstrated that, among the input factors, the T parameter had the most pronounced effect on the model's output. Even small changes in the value of T led to considerable fluctuations in the predicted BC.

Enhancing parameter identification for proton exchange membrane fuel cell using modified manta ray foraging optimization

In this paper, an accurate model of proton exchange membrane fuel cell (PEMFC) for optimal identification of PEMFC parameters has been developed. The optimization methodology is based on the modified version of Manta Ray Foraging Optimization (MMRFO) technique for minimizing the sum of squared errors (SSE) between the Experimentally measured stack voltage and the estimated voltage produced by the optimized model. In the modified methodology, the sine-cosine method has been utilized to enhance the global searching capability in the exploration phase and the local searching capability in the exploitation phase of the MRFO algorithm. In order to validate the effectiveness of the suggested methodology, four different case studies comprising standard benchmark 250 W PEMFC, BCS-500 W PEMFC, SR-12 500 W FC, and 1 kW Temasek stacks were utilized, and the attainments have been compared with the measured polarization characteristics. The attainments have been intensively compared with several metaheuristic algorithms (MA) including Tree growth Algorithm (TGA), Grey wolf optimizer (GWO), Whale optimization algorithm (WOA), Salp swarm algorithm (SSA), and original Manta Ray Foraging Optimization (MRFO), to confirm the superiority of the MMRFO against the compared techniques. The obtained results give a satisfactory agreement between the MMRFO-based model and the experimentally measured data. Finally, the achievements confirmed the effectiveness of the MMRFO over the basic MRFO algorithm and other novel metaheuristic algorithms in identifying PEMFC parameters.

Energy-Efficient Clustering in Wireless Sensor Networks Using Grey Wolf Optimization and Enhanced CSMA/CA.

Survivability is a critical concern in WSNs, heavily influenced by energy efficiency. Addressing severe energy constraints in WSNs requires solutions that meet application goals while prolonging network life. This paper presents an Energy Optimization Approach (EOAMRCL) for WSNs, integrating the Grey Wolf Optimization (GWO) for enhanced performance. EOAMRCL aims to enhance energy efficiency by selecting the optimal duty-cycle schedule, transmission power, and routing paths. The proposed approach employs a centralized strategy using a hierarchical network architecture. During the cluster formation phase, an objective function, augmented with GWO, determines the ideal cluster heads (CHs). The routing protocol then selects routes with minimal energy consumption for data transmission to CHs, using transmission power as a metric. In the transmission phase, the MAC layer forms a duty-cycle schedule based on cross-layer routing information, enabling nodes to switch between active and sleep modes according to their network allocation vectors (NAVs). This process is further optimized by an enhanced CSMA/CA mechanism, which incorporates sleep/activate modes and pairing nodes to alternate between active and sleep states. This integration reduces collisions, improves channel assessment accuracy, and lowers energy consumption, thereby enhancing overall network performance. EOAMRCL was evaluated in a MATLAB environment, demonstrating superior performance compared with EEUC, DWEHC, and CGA-GWO protocols, particularly in terms of network lifetime and energy consumption. This highlights the effectiveness of integrating GWO and the updated CSMA/CA mechanism in achieving optimal energy efficiency and network performance.

Optimal allocation of EV charging station and capacitors considering reliability using a hybrid optimization approach

This paper proposes a novel approach for strategically placing EV charging stations (EVCS) and capacitors within electrical distribution networks (EDN) to mitigate issues arising from the widespread adoption of Electric Vehicles (EVs). The goal is to maximize the net present value (NPV) by reducing energy losses and minimizing system interruption costs while ensuring reliability. To achieve this, the paper introduces a hybrid optimization technique called HGP_CS, which combines Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Cuckoo Search optimization methods. By employing various compensation coefficients to assess failure rates and identify strategies for enhancing NPV, the approach aims to optimize the placement of EVCS and capacitors within the EDN. Validation is carried out in IEEE 33-bus and 118-bus systems to demonstrate the performance of the proposed approach. It is found that the proposed approach, compared to the base case, reduced the energy loss cost by 34.19% and 31.57% and also the Expected Interruption Costs (EIC) by 17.26% and 13.41%, for IEEE 33-bus and 118-bus system, respectively. Additionally, the proposed method is economically beneficial as it can attain higher profits. In IEEE 33- bus and 118 bus systems, the annual net profit using the proposed approach is 36,805 $/year and 271,670.72 $/year, respectively. Thus, the proposed technique enhances the overall system performance and significantly boosts profitability, making it a compelling choice for optimizing the placement of charging stations and reactive power sources in networks.

An Adaptive Control Based on Improved Gray Wolf Algorithm for Mobile Robots

An Adaptive Control Based on Improved Gray Wolf Algorithm for Mobile Robots

Spatiotemporal plasticity of prey selection in the wolf

AbstractUnder an optimal foraging scenario, prey selection would be expected to occur when food resources are abundant. A positive frequency‐dependent prey selection would elicit prey switching when the abundance of main food resources decreases, potentially favouring community resilience to the effects of intensive, selective predation on a single prey. We assessed whether a positive frequency‐dependent prey selection by the wolf Canis lupus occurred in two areas hosting abundant populations of wild ungulates, one in northern (Słowiński National Park, Poland) and the other one in southern (Maremma Regional Park, Italy) Europe, throughout a cold semester. In Słowiński, ungulate community was dominated by red deer Cervus elaphus (57% availability) over wild boar Sus scrofa (35%) and roe deer Capreolus capreolus (8%); wild boar and fallow deer Dama dama (43–37%) were more abundant than roe deer (20%) in Maremma. In both areas, wolf diet was dominated by wild ungulates, with a major use of red deer in Słowiński and wild boar in Maremma. Prey selection occurred in both areas, and it was addressed towards the most abundant prey in Słowiński, that is, the red deer, but only towards the wild boar in Maremma, where the fallow deer was used according to availability. In Slowinski, high red deer density may have driven wolf prey selection, while the shifting of activity rhythms of the fallow deer in the Maremma as antipredator response to wolf presence may have reduced predation. Despite its comparable densities between the two areas, the wild boar was selected in Maremma and under‐used in Słowiński. Results provide partial support to positive frequency‐dependent selection, emphasising the spatiotemporal plasticity of wolf–prey relationships. The relative role of prey density and other factors (e.g., antipredator behavioural responses) should be assessed at longer temporal scales.

A Novel gray wolf optimization-based key frame extraction method for video classification using ConvLSTM

A Novel gray wolf optimization-based key frame extraction method for video classification using ConvLSTM

Optimizing cluster head selection for energy efficiency in wireless sensor networks: A hybrid algorithm combining grey wolf and enhanced sunflower optimization

AbstractIn this letter, we introduce a novel cluster head selection algorithm namely mixed grey wolf and improved sunflower optimization algorithm (MGWISFO). This algorithm leverages both energy requirements and inter‐node distances to select cluster heads (CH). Within this algorithm, the Grey Wolf Optimizer facilitates exploration, offering a broader search, while the improved Sunflower Optimization focuses on exploitation, delivering a narrower search. This balance between exploration and exploitation leads to the identification of the optimal CH node, thereby enhancing network performance. To validate its effectiveness, the proposed algorithm is benchmarked against existing strategies such as particle swarm optimization (PSO), genetic algorithm (GA), grey wolf optimization (GWO), and sunflower optimization (SFO) across various performance parameters including throughput, the number of live and dead nodes, and residual energy. Simulation results unequivocally establish the unparalleled performance of our proposed algorithm, surpassing the capabilities of existing algorithms.

Metaheuristic tuned decentralized PID controller based active suspension system for railway vehicle

AbstractA high‐speed railway system is one of the sustainable alternatives to other modes of transportation and may connect the most congested urban cities with minimum carbon emissions. However, the vibration intensity increases as the train's operating speed increases, resulting in deteriorated ride comfort and stability. Hence, this article investigates a 27‐degree‐of‐freedom (DOF) dynamic model of railway vehicles with an improved active suspension system. The decentralized control structure performs the controlling action with five optimized Proportional Integral Derivative (PID) controllers that suppress the vehicle body's vertical, lateral, pitch, roll, and yaw motions. Further, to optimize the PID parameters, three metaheuristic optimization techniques, Genetic algorithm (GA), Grey Wolf Optimization (GWO), and Flower Pollination Algorithm (FPA), are utilized, and their simulated results are compared with the passive system as well as other conventional tuning technique. Moreover, the performance of the proposed control strategy is evaluated in the frequency domain under random track irregularities, and the results are characterized in terms of power spectral densities (PSDs). The simulated results show that among the proposed metaheuristic algorithms, FPA outperforms with a significant reduction in vehicle vibration compared to other tuning methods. The percentage reduction of the vertical, lateral, pitch, rolls, and yaw accelerations is 71.4%, 35.1%, 52.8%, 48.1%, and 38.2%, respectively, ensuring enhanced vehicle ride comfort.

Real Power Losses Reduced by Network Reconfiguration the Distribution Systems using Modified BAT Algorithm

This research paper is proposed to achieving the minimum power losses in all the branches, minimum number of switching operations, maximizing the power flow through the placing the DG sources, minimizing the voltage deviations with satisfying all the constraints using the modified BAT algorithm. The effect of the offered method is tested on standard systems like IEEE 33, 69 buses and Indian standard 62 bus distribution systems. The mBAT effect is estimated with the recent algorithm including Shuffled Frog, Stud krill, Dingo, Grey Wolf, and Antlion algorithms. MATLAB results are proved that the total power active power losses and branch voltages and number of switches, capacity of DG sources and cost of the DG sources are drastically reduced. The results are compared with many techniques are tabulated. Moreover, the mBAT algorithm is more superior and confirmed with other animal-related algorithms like Antlion, Grey wolf, Gross Hopper, Dingo, sturd Krill, cuckoo crunch algorithm and Shuffled Frog algorithms. In view of more speed of convergence and high accuracy and processed in less number repetition. Also, the results of the proposed techniques are encouraging and helpful to future research.

Energy efficient clustering and routing protocol based on quantum particle swarm optimization and fuzzy logic for wireless sensor networks

Clustering and routing protocols play a pivotal role in reducing energy consumption and extending the lifespan of wireless sensor networks. However, optimizing energy efficiency to maximize network longevity remains a primary challenge for these protocols. This paper introduces QPSOFL, a clustering and routing protocol that integrates quantum particle swarm optimization and a fuzzy logic system to enhance energy efficiency and prolong network lifespan. QPSOFL employs an enhanced quantum particle swarm optimization algorithm to select optimal cluster heads, utilizing Sobol sequences for population diversification during initialization. Additionally, it incorporates Lévy flight and Gaussian perturbation-based position updates to prevent trapping in local optima. Benchmark experiments validate QPSOFL's efficacy compared to Harris Hawks Optimization (HHO), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Quantum Particle Swarm Optimization (QPSO), focusing on accuracy, search capability, and convergence speed. Within QPSOFL, a fuzzy logic system determines the best next-hop cluster head based on descriptors such as residual energy, energy deviation, and relay distance. Extensive simulations compare QPSOFL's performance in terms of network lifetime, throughput, energy consumption, and scalability against existing protocols E-FUCA, IHHO-F, F-GWO, and FLPSOC, demonstrating its superior performance over these counterparts.

A comprehensive analysis of the optimal GWO based FOPID MPPT controller for grid-tied photovoltaics system under atmospheric uncertainty

Recent years have seen an increase in the deployment of renewable energy sources on power system networks as a result of the gradual development of renewable energy sources. In order to utilize these intermittent sources, grid integration is crucial, since weather conditions are hard to predict accurately and do not match the pattern of generation. As a result, controlling energy becomes an unavoidable challenge when the alternative energy source is connected to the grid in a distributed manner. A number of factors contribute to the problem, including sporadic sources, daytime expenses, and limitations concerning the specifications of solar panels. Therefore, a more robust control system is needed to enhance the reliability and efficiency of the renewable energy integrated power network. Thus, this manuscript presents the design, implementation, and performance of an improved fractional order PID (FOPID) controller for proposed grid-connected photovoltaic systems. To maximize the power from the photovoltaic source, the controller is designed to extract as much energy as possible. In addition to having the adaptive nature of a PID controller, the proposed FOPID controller is capable of optimizing its gain parameter according to the generator and grid side parameters being considered. The proposed study utilized grey wolf optimization (GWO) to tune the FOPID controller for tracking the quadrature axis model, DC link voltage and current regulation, and maximizing the maximum power point. Further, FOPID is used to perform the system's current control functions, and each time the error is measured, the regulating parameters are updated accordingly. This paper contrasts fuzzy logic controllers (FLC), flying squirrel search optimization (FSSO), and PSO-tuned FOPID controllers with the proposed work. The simulation's results are displayed and examined in the section on results and outcomes. The obtained results demonstrate maximum solar power output under erratic weather conditions, validating the effectiveness of the proposed controller.

Improving quality of service for Internet of Things(IoT) in real life application: A novel adaptation based Hybrid Evolutionary Algorithm

In this paper, we address critical challenges in IoT sensor lifespan, service latency, and coverage area, all impacting energy consumption in smart agriculture applications. To enhance the quality of service (QoS) while prolonging the energy efficiency of smart sensors, a novel optimization algorithm is introduced. Referred to as the ”Adaptation-Based Hybrid Evolutionary Algorithm,” this innovative approach combines the strengths of Grey Wolf Optimizers (GWO) and Differential Evolution (DE) algorithms. The methodology involves a new adaptation-based strategy and incorporates a hybrid algorithm that synergizes the exploratory and exploitative capabilities of both GWO and DE algorithms. This hybrid approach is leveraged to meticulously select optimal mutation new adaptation services, drawing from the GWO and DE algorithm frameworks. Notably, the algorithm’s control parameters autonomously adjust through insights gained from prior evolutionary searches. Furthermore, we enhance the DE-based crossover technique by integrating the proficient search capabilities of the GWO algorithm, renowned for tackling continuous global optimization problems. To validate our approach, we apply it to IoT scenarios and optimize QoS through a fitness function that comprehensively accounts for energy consumption, coverage rate, lifespan, and latency. Comparative evaluations against standard algorithms underscore the superior performance of our proposed methodology, particularly evident in its application to IoT-smart agriculture settings.