To address performance degradation and control instability in electro-hydraulic servo active suspension systems due to internal leakage faults arising from wear and aging of hydraulic components, this paper proposes an innovative fuzzy PID fault-tolerant controller based on the Improved Giant Armadillo Optimization (IGAO) algorithm. Specifically, to overcome the limitations of the standard Giant Armadillo Optimization (GAO), which is prone to local optima and exhibits poor convergence performance when handling multi-constraint parameter optimization problems, this study introduces a nonlinear dynamic inertia weight mechanism and a random reflection strategy for out-of-bounds particles to improve the original algorithm’s performance. These enhancements significantly enhance its ability to balance global exploration and local exploitation. Furthermore, this research develops a comprehensive performance evaluation fitness function by quantifying key performance indicators such as body acceleration, suspension dynamic deflection, and tire dynamic load. A quarter-car model incorporating an internal leakage fault was established as a simulation validation platform to demonstrate the reliability of the proposed method. Simulation results indicate that under various road excitation conditions, the proposed IGAO algorithm can rapidly and stably converge to superior parameters for the fuzzy PID controller. Compared to the Particle Swarm Optimization (PSO) and standard GAO algorithm, the control system optimized by IGAO not only significantly more effectively suppresses body vibration and reduces shock amplitude but also exhibits stronger dynamic recovery performance and control robustness under varying degrees of internal leakage faults. This research provides a robust control approach for addressing internal parameter uncertainties in hydraulic systems and offers a new approach to theoretical modeling for enhancing the reliability of design and fault-tolerant control capabilities of active suspension systems.

Urban traffic congestion, along with the resulting fuel waste and pollution, poses increasing challenges as city populations grow. There is a critical need for techniques that mitigate these effects while maintaining traffic efficiency. This study proposes an integrated framework that combines the Internet of Things (IoT) infrastructure and the Internet of Drones (IoD) to enhance urban traffic management. IoT sensors monitor real-time traffic conditions, while Roadside Units (RSUs) collect and process data to deliver timely updates to vehicles. Drones dynamically extend communication coverage by acting as mobile relay nodes, accelerating traffic information dissemination across wider areas, particularly those with sparse connectivity. Drone placement was optimized for maximum coverage using the Particle Swarm Optimization (PSO) algorithm. Experiments conducted in two urban scenarios-Dammam (Saudi Arabia) and Doha (Qatar)-used the SUMO simulator, Traffic Control Interface (TraCI), and Python to implement communication protocols and adaptive rerouting mechanisms. The findings suggest that integrating IoT and drones can substantially reduce travel delays and vehicular emissions while maintaining real-time operational efficiency. In Dammam, emissions and travel times decreased by up to 40.99% and 32.05%, respectively, and in Doha by up to 48.78% and 43.92%. These results indicate that coordinated IoT-IoD systems can support sustainable urban mobility by improving traffic flow and contributing to cleaner air.

This study proposes a Fractional Calculus Hybrid Approach to enhance the dynamic and thermal performance of vector-controlled Permanent Magnet Synchronous Motor (PMSM) drives. The method employs a Fractional Hybrid Particle Swarm Optimization (FHPSO) algorithm that embeds fractional-order calculus within the conventional PSO structure and hybridizes it with Simulated Annealing (SA) to reinforce convergence stability and global exploration. By precisely tuning the Proportional-Integral (PI) controller parameters, the proposed algorithm achieves significant reductions in overshoot and settling time, thereby improving both transient and steady-state drive responses. Simultaneously, a detailed thermal model of the Variable Frequency Drive (VFD) is developed to predict and regulate temperature variations under diverse load and speed conditions, ensuring efficient heat dissipation and enhanced cooling. The integrated control-thermal co-optimization yields a 13.3% to 0.98% and a ~ 64% improvement in temperature regulation (reducing MOSFET temperatures from over 80°C to ~ 29°C), validated through comprehensive simulations and Hardware-in-the-Loop (HiL) experiments. Overall, the proposed approach demonstrates that fractional-order hybrid optimization provides a mathematically rigorous and computationally efficient pathway toward improving energy efficiency, thermal reliability, and long-term durability of PMSM-based VFD systems for advanced electric vehicle applications.

Adaptive correction of wind power equipment digital twin model based on particle swarm optimization algorithm

Optimization of fuel cell bio-inspired bird belly-shaped obstacle flow channel structure through back-propagation neural network model optimized by the particle swarm optimization algorithm

A cross-cultivar hyperspectral framework for huanglongbing detection in citrus via wavelength optimization and deep learning.

Optimizing cut order planning in apparel manufacturing: A particle swarm optimization and genetic algorithm approach

A novel Quantum Beta distributed multi-objective Particle Swarm Optimization algorithm for fake accounts detection

A multi-strategy Particle Swarm Optimization algorithm for three-dimensional path planning of amphibious unmanned aerial vehicles

A matrix-assisted surrogate particle swarm optimization algorithm for multi-objective deployment of solar insecticidal lamps

Virtual power plants efficiently aggregate distributed energy resources with small capacities but large quantities to participate in electricity market transactions through advanced control technologies. As the number of distributed power sources increases, issues such as output volatility and optimal decision-making need to be addressed. To tackle these problems, this paper proposes an optimal economic dispatch strategy for virtual power plants that accounts for flexible resource responses under uncertain scenarios. First, a combined prediction model based on variational mode decomposition (VMD) and an improved bidirectional multi-gated long short-term memory network is established to achieve accurate prediction of renewable energy output. On this basis, a price–demand elasticity matrix is constructed to characterize the spatiotemporal coupling effect of time-of-use electricity prices on load, and a demand response model based on optimal time-of-use electricity pricing is established. Meanwhile, an improved Particle Swarm Optimization (PSO) algorithm is employed to achieve efficient and precise solutions. Finally, the effectiveness and feasibility of the proposed method are validated and illustrated through an improved IEEE-33 bus test system.

Soybean lodging severely impairs yield and quality, and its precise grading is a key prerequisite for intelligent agricultural management and loss assessment in agricultural insurance. Most existing studies have focused primarily on soybean lodging identification and lodging resistance evaluation, whereas methods for the precise differentiation of lodging grades remain to be refined. This study presents an improved AlexNet model integrated with a Local Feature Aggregation (LFA) attention mechanism and a dynamic optimization strategy for the accurate grading of soybean lodging. RGB imagery of soybean canopies during the grain-filling to early maturity stages was acquired via a multispectral unmanned aerial vehicle (UAV). A dynamic Dropout strategy was adopted to enhance model stability and mitigate overfitting, and the Particle Swarm Optimization (PSO) algorithm was employed to intelligently optimize key hyperparameters of the model. The results demonstrate that the optimized model achieved an overall accuracy of 94.23% on the test set, with an average loss of 0.0682 and an inference speed of 0.422 s/step. In independent field validation, the grading accuracies for the five lodging grades were 90.12%, 86.35%, 89.47%, 88.93%, and 92.76%, respectively, with a mean accuracy of 89.53%. The proposed model enables the rapid and precise grading of soybean lodging under field conditions, thereby providing effective technical support for intelligent field management and disaster loss assessment in soybean production.

This article focuses on the current wastewater treatment process, which has many water quality targets. In order to meet the demand for environmental protection and energy conservation, the system energy consumption, including aeration process energy consumption and pump energy consumption, is integrated into the output target of the treatment model. A framework system for online control and monitoring of the wastewater treatment system is designed. Firstly, the functions of the entire system framework are described in detail according to the data processing and online monitoring scheme. Then, an adaptive intelligent wastewater parameter dynamic optimization model is constructed to address the problem of multiple target parameters in the wastewater treatment process. This article uses an improved particle swarm optimization algorithm to dynamically change the inertia weight of each target parameter, improve the global search ability of particles, and enhance Pareto The distribution and convergence of the solution, as well as the introduction of crowding distance sorting method, are used to maintain the external elite set and update the optimal global value, better maintaining the diversity and convergence of the population. At the same time, self-organizing optimization strategy is integrated into the model, and the detailed design process of the model is given. Finally, based on Matlab simulation model, the treatment effect of key sewage parameters and the prediction results of sewage treatment parameters under dynamic disturbance are analyzed, proving the effectiveness of the proposed method in this paper.

Pneumatic drives remain widely used in industrial automation due to their simplicity and reliability, yet their overall energy efficiency is typically low. This study introduces an energy-efficient pneumatic drive concept that enhances braking control and enables compressed air recovery without modifying the actuator’s mechanical design. A transient one-dimensional mathematical model is developed to describe system dynamics and is combined with a particle swarm optimization (PSO) algorithm to determine optimal switching coordinates for the braking phase under constraints on piston motion and positioning accuracy. To assess the validity and limitations of simplified models, the optimized process is additionally investigated using a three-dimensional CFD model with moving mesh and valve control. The CFD model is validated experimentally using pressure measurements in the cylinder chambers. The results reveal that conventional isothermal 1D models underestimate transient pressure and energy parameters by up to 30–35% in systems with air recovery, highlighting the necessity of 3D analysis for accurate energy assessment. Optimization increases the duration of the recovery phase by a factor of 2.8 while maintaining cycle time and improving positioning accuracy. The resulting cycle energy efficiency reaches 53.4%, significantly exceeding typical industrial values. The proposed methodology provides a practical framework for designing energy-efficient pneumatic drives.

Abstract Epoxy resin offers outstanding electrical properties and mechanical strength, making it a widely adopted insulating material in high-voltage equipment. However, its limited thermal conductivity restricts its broader utilization in ultra-high-voltage applications. The development of epoxy resins with both high thermal conductivity and electrical insulation has traditionally relied on empirical trial-and-error approaches involving filler formulation and complex characterization. Hence, this study proposes a method for designing and predicting the electro-thermal performance of epoxy resin composite materials utilizing particle swarm optimization (PSO) and eXtreme Gradient Boosting (XGBoost). Finite element simulation (FES) is utilized to calculate the electro-thermal properties of epoxy resin composite samples, thereby generating a dataset for model training. The XGBoost model trained on this dataset delivers the highest predictive accuracy and robustness, with its hyperparameters optimized by the particle swarm optimization (PSO) algorithm. Additionally, SHapley Additive exPlanations (SHAP) analysis is employed to quantify the contribution of input features to the electro-thermal performance. The experimental validation of the SHAP-derived trends is conducted using AlN-doped epoxy resin samples. Overall, this study improves the efficiency of the optimization for epoxy resin composites and offers a method for developing advanced composite materials.

We theoretically propose and experimentally demonstrate an ultra-broadband metamaterial absorber (MA) based on a quadrilayer structure, comprising a Cr-SiO2-Ge cube array atop a Ge-Cr base film. The geometric parameters were optimized using a hybrid particle swarm optimization (PSO) and gradient descent algorithm. The resulting absorber exhibits a simulated average absorption of 95.1%, which is matched by a measured average absorption of 84.1% across the ultraviolet to near-infrared spectrum (200-2000 nm). The simulated absorber exhibits a 90% absorption bandwidth of 1413 nm (587-2000 nm), along with polarization insensitivity and excellent angular stability under both TE and TM modes. Furthermore, quantitative analysis reveals a high solar-weighted absorption of 91.8%. Finally, the efficient generation of photoelectrons and hot carriers-facilitated by the Ge and Cr layers, respectively-highlights the device's potential for applications in solar energy harvesting, photodetection, and photothermal conversion.

With the acceleration of urbanization and the increase in car ownership, traffic management plays a crucial role in the urbanization process. Traditional traffic flow prediction methods are mainly based on historical data and statistical models. The Traffic Flow Forecasting Dataset was selected with data from 36 sensors on two highways in the Northern Virginia/Washington, D.C., U.S. Capital Region, measured every 15 minutes and covering 47 characteristics, including historical traffic volume sequences, time, roads, and more. The GCN part of the model architecture is set up with two layers, the first layer preliminarily extracts the spatial correlation of transportation network nodes, and the second layer further excavates the deep spatial dependence, and the input dimension is 47 dimensions, which corresponds to the feature dimension of the dataset. In the optimization process, the parameters of the GCN are optimized by the PSO algorithm, and the learning rate, convolution kernel and other parameters are adjusted to improve the accuracy of the model's prediction of urban traffic flow.By analyzing the changes in traffic flow in historical traffic data, a statistical model is established to predict future traffic flow. Standard methods include time series analysis, regression analysis, and neural networks. However, these methods have significant limitations regarding prediction accuracy and real-time performance and cannot adapt to the dynamic changes in the transportation system. Therefore, this study proposes a city traffic flow prediction model based on the combination of Graph Convolutional Network (GCN) and Particle Swarm Optimization (PSO) algorithm. GCN captures spatial dependencies in the traffic network, and the PSO algorithm is used to optimize model parameters and improve prediction performance. The research experimental results show that compared to a single GCN model, the optimized GCN-PSO model has significantly improved prediction accuracy, with a 15.3% reduction in mean square error (MSE) and a 12.7% reduction in mean absolute error (MAE). In real-time prediction scenarios, the response time of the GCN-PSO model was reduced by 8.9%, effectively improving prediction efficiency. Meanwhile, analyzing traffic data from different cities and time periods verified the universality and stability of the GCN-PSO model in various scenarios.

There are issues with increased energy consumption of terminal equipment in the oil depot information system, as well as issues that are not conducive to the intelligent fire warning of this system. In this regard, edge computing is introduced, multi platform task uninstallation algorithm is designed, and a mathematical model is built. Quantum particle swarm optimization algorithm is used for optimization solution to determine the intelligent uninstallation strategy for multi platform tasks. An intelligent fire warning algorithm based on quantum particle swarm optimization and back propagation neural network is constructed to judge the fire situation. In the simulation results, quantum particle swarm optimization algorithm has significant advantages in multi platform task uninstallation. Compared with particle swarm optimization, quantum particle swarm optimization can reduce energy consumption by up to 17.1%. Compared with completely local algorithms, this research algorithm has saved 13.5%, 24.3%, and 38.3% of energy consumption, indicating the effectiveness of this research method algorithm. The mean squared error of the back propagation neural network optimized by quantum particle swarm optimization algorithm and the back propagation reached the expected error value in 106 iterations and 180 iterations respectively. The former has better convergence and global search ability than those of the latter. The back propagation neural network model optimized by quantum particle swarm optimization algorithm can effectively identify open fire, smoldering fire, and non-fire situations, and there is no false or missed reporting. This indicates that the research method can be beneficial for the intelligent fire warning of oil depot fire information system and promote the operational safety of oil depot.

With the acceleration of urbanization and the frequent urban emergencies, traditional rescue path planning methods have response delay and low path efficiency. To improve the efficiency of urban emergency rescue and resource scheduling capabilities, a path planning technique for urban emergency rescue based on improved Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) is built. The research introduces Tent chaotic mapping initialization particles, adaptive segmented inertia weights and exponential learning factors for the PSO algorithm to enhance global search capabilities, and adds traction acceleration terms to avoid local optimality. For the GA, a combination strategy of elite retention and roulette is used to improve selection efficiency, and the Metropolis criterion is combined to optimize the cross-mutation operation, and an adaptive variable neighborhood search mechanism is introduced to strengthen local search. In the experimental setting, the Python simulation platform is used to compare with baseline methods such as RRT*, D* Lite and MOPSO. The test indicators include response time, planning success rate, path length, number of convergence iterations, etc. Experimental results showed that under six concurrent events, the response time of the research method was 6 seconds, which was significantly better than that of the comparison method. When the dynamic obstacle density was 40/km², the planning success rate reached 90.2%. When the scene complexity was 200 nodes, the single planning calculation time was 150ms. The research method converged at the 100th iteration, and the fitness change rate was reduced to 1.3%, showing faster convergence speed and better stability. The above results show that the proposed method is superior to traditional methods in terms of timeliness, robustness and optimization capabilities, which is suitable for emergency rescue path planning in complex urban scenarios.

ABSTRACT Five-axis milling of free-form surfaces requires simultaneous optimization of toolpaths and continuously varying tool orientations. However, most commercial CAM workflows focus only on geometric simulation and neglect critical physical quantities such as cutting forces, leading to force spikes, tool deflection and surface inaccuracies. This study proposes an integrated optimization framework that combines automatic toolpath generation from STEP/B-Rep models with solid-model-based extraction of cutter–workpiece engagement (CWE). The CWE data are transformed into entry and exit immersion parameters and undeformed chip thickness to enable mechanistic cutting-force prediction. Tool orientations are then optimized using a curvature-aware parameterization method and a particle swarm optimization (PSO) algorithm. Numerical validation on representative free-form surfaces demonstrated that the proposed method reduced the maximum cutting force from 214 N to 170 N (a 20.6% reduction) compared with the original path. A secondary optimization stage incorporating polynomial-fitted force smoothing decreased force fluctuation amplitude by over 40%, resulting in both smoother tool-axis trajectories and improved machining stability. By integrating geometric modeling, physical simulation and metaheuristic optimization, the proposed PSO-based framework provides a quantitatively verified improvement in force-aware toolpath planning. The approach can be readily incorporated into existing CAD/CAM environments for efficient and reliable five-axis machining of complex free-form surfaces.