Search Results (6,153)

The integration of Reconfigurable Intelligent Surfaces (RISs) into wireless networks is a promising paradigm for enhancing spectral efficiency and coverage in beyond-5G systems. However, in multiuser downlink scenarios, the joint optimization of discrete RIS phase shifts and user scheduling presents a high-dimensional combinatorial challenge due to their tight coupling, which is often intractable with conventional methods. Furthermore, conventional RISs are limited by their unidirectional signal reflection, creating coverage blind spots. To address these issues, this paper first investigates a multi-user scheduling system assisted by a conventional RIS. We employed a vector projection relaxation method to transform the complex joint optimization problem, and then used an algorithm based on particle swarm optimization to jointly optimize the discrete phase shift and user scheduling. Simulation results demonstrate that this proposed algorithm significantly improves the system’s achievable data rate compared to existing benchmarks. Subsequently, to overcome the fundamental coverage limitation of conventional RISs, we extend our framework to two advanced systems: double-RIS and Simultaneously Transmitting and Reflecting RIS (STAR-RIS). For the STAR-RIS system, leveraging its energy-splitting protocol, we develop a novel joint optimization algorithm for phase shifts, amplitudes, and user scheduling based on an alternating optimization framework. This constitutes another significant contribution, as it effectively manages the added complexity of simultaneous transmission and reflection control. Simulations confirm that the STAR-RIS-assisted system, optimized by our algorithm, not only eliminates coverage blind spots but also surpasses the performance of traditional RIS, offering new perspectives for optimizing next-generation wireless communication networks. Full article

To address the problems of local optima and insufficient convergence accuracy in parameter identification of primary frequency regulation (PFR) for steam turbines, this paper proposed a hybrid identification method that integrated an Improved Bayesian Optimization (IBO) algorithm and an Improved Whale Optimization Algorithm (IWOA). By initializing the Bayesian parameter population using Tent chaotic mapping and the reverse learning strategy, employing a radial basis kernel function hyperparameter training mechanism based on the Adam optimizer and optimizing the Expected Improvement (EI) function using the Limited-memory Broyden–Fletcher– Goldfarb–Shanno with Bounds (L-BFGS-B) method, IBO was proposed to obtain the optimal candidate set with the smallest objective function value. By introducing a nonlinear convergence factor and the adaptive Levy flight perturbation strategy, IWOA was proposed to obtain locally optimized optimal solutions. By using the reverse-guided optimization mechanism and employing a fitness-oriented selection strategy, the optimal solution was chosen to complete the closed-loop process of reverse learning feedback. Nine standard test functions and the Proportional Integral Derivative (PID) parameter identification of the electro-hydraulic servo system in a 330 MW steam turbine were presented as examples. Compared with Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Bayesian Optimization (BO) and Particle Swarm Optimization-Grey Wolf Optimizer (PSO-GWO), the Improved Bayesian Optimization-Whale Optimization Algorithm (IBO-WOA) proposed in this paper has been validated to effectively avoid the problem of getting stuck in local optima during complex optimization and has high parameter recognition accuracy. Meanwhile, an Out-Of-Distribution (OOD) Test based on noise injection had demonstrated that IBO-WOA had good robustness. The time constant identification of the steam turbine were carried out using IBO-WOA under two experimental conditions, and the identification results were input into the PFR model. The simulated power curve can track the experimental measured curve well, proving that the parameter identification results obtained by IBO-WOA have high accuracy and can be used for the modeling and response characteristic analysis of the steam turbine PFR. Full article

Deception detection is considered a concern for all individuals in their everyday lives, as it greatly affects human interactions. While multiple automatic lie detection systems exist, their accuracy still needs to be improved. Additionally, the lack of adequate and realistic datasets hinders the development of reliable systems. This paper presents a new multimodal dataset with physiological data (heart rate, galvanic skin response, and body temperature), in addition to demographic data (age, weight, and height). The presented dataset was collected from 49 unique subjects. Moreover, this paper presents a polygraph-based lie detection system utilizing multimodal sensor fusion. Different machine learning algorithms are used and evaluated. Random Forest has achieved an accuracy of 97%, outperforming Logistic Regression (58%), Support Vector Machine (58% with perfect recall of 1.00), and k-Nearest Neighbor (83%). The model shows excellent precision and recall (0.97 each), making it effective for applications such as criminal investigations. With a computation time of 0.06 s, Random Forest has proven to be efficient for real-time use. Additionally, a robust k-fold cross-validation procedure was conducted, combined with Grid Search and Particle Swarm Optimization (PSO) for hyperparameter tuning, which substantially reduced the gap between training and validation accuracies from several percentage points to under 1%, underscoring the model’s enhanced generalization and reliability in real-world scenarios. Full article

Nature has evolved sophisticated optimization strategies over billions of years, yet computational algorithms inspired by plants remain remarkably underexplored. We present a comprehensive systematic review following PRISMA 2020 guidelines, analyzing 175 studies (2000–2025) of plant-inspired metaheuristic optimization algorithms and their predominantly animal-inspired counterparts. Despite constituting only 9.7% of bio-inspired optimization literature, plant-inspired algorithms demonstrate competitive and often superior performance compared to animal-inspired approaches. Through a meta-analysis of empirical studies, we document that algorithms like Phototropic Growth and Binary Plant Rhizome Growth achieve 97% superiority on CEC2017 benchmarks and 81% accuracy on high-dimensional feature-selection tasks—significantly exceeding established animal-inspired methods like Particle Swarm Optimization and Genetic Algorithms (p < 0.05). However, our review reveals a critical gap: the majority of these algorithms lack the formal theoretical foundations of their counterparts. This paper systematically documents these theoretical deficiencies and positions them as a key area for future research. Our framework maps botanical processes to computational operators, providing structured guidance for future algorithm development. Plant-inspired approaches excel particularly in distributed optimization, resource allocation, and multi-objective problems by leveraging unique mechanisms evolved for survival in sessile, resource-limited environments. These findings establish plant-inspired approaches as a promising yet severely underexplored frontier in optimization theory, with immediate applications in sustainable computing, resilient network design, and resource-constrained artificial intelligence. Full article

In the hot strip rolling process, inter-stand speed coordination directly affects product quality and production stability. Traditional linear speed compensation strategies perform poorly under extreme conditions such as strip tension and strip piling, making it difficult to maintain stable loop control. This study proposes a speed compensation strategy that integrates Particle Swarm Optimization (PSO) with Model Predictive Control (MPC). Based on the mechanism of hot rolling, a nonlinear state-space model is constructed, in which the compensation parameter is treated as an optimization variable to formulate a rolling optimization problem. PSO is employed to globally solve the nonlinear MPC problem, yielding an optimal compensation sequence that adapts to disturbance variations. The proposed algorithm can adaptively adjust the speed compensation parameter under typical strip piling and strip tension conditions, thereby achieving stable loop regulation and maintaining the looper angle within the desired range. This effectively addresses the speed coordination problem under abnormal conditions in hot strip rolling, improving the control performance. The experimental results verify the effectiveness of the proposed method. Full article

The joint optimization of storage location assignment and order picking efficiency for fresh products has become a vital challenge in intelligent warehousing because of the perishable nature of goods, strict temperature requirements, and the need to balance cost and efficiency. This study proposes a comprehensive mathematical model that integrates five critical cost components: picking path, storage layout deviation, First-In-First-Out (FIFO) penalty, energy consumption, and picker workload balance. To solve this NP-hard combinatorial optimization problem, we develop a Particle Swarm-guided hybrid Genetic-Simulated Annealing (PS-GSA) algorithm that synergistically combines global exploration by Particle Swarm Optimization (PSO), population evolution of Genetic Algorithm (GA), and the local refinement and probabilistic acceptance of Simulated Annealing (SA) enhanced with Variable Neighborhood Search (VNS). Computational experiments based on real enterprise data demonstrate the superiority of PS-GSA over benchmark algorithms (GA, SA, HPSO, and GSA) in terms of solution quality, convergence behavior, and stability, achieving 4.08–9.43% performance improvements in large-scale instances. The proposed method not only offers a robust theoretical contribution to combinatorial optimization but also provides a practical decision-support tool for fresh e-commerce warehousing, enabling managers to flexibly weigh efficiency, cost, and sustainability under different strategic priorities. Full article

This paper proposes two improved algorithms, the DE-PSO algorithm, which combines differential evolution and phased strategy, and the hybrid particle swarm optimization algorithm integrating whale algorithm (WOAPSO), which combines the whale optimization mechanism. Compared to traditional calibration methods (such as the Newmark three- and two-parameter methods), which rely on empirical simplified models, adapting them to the complex seismic nonstationarity and multipeak characteristics is difficult. However, although intelligent optimization algorithms, such as particle swarm optimization (PSO) and differential evolution (DE) have improved calibration accuracy in recent years, insufficient convergence stability and low computational efficiency, among other problems, persist. Therefore, based on experiments, the performances of these algorithms were compared with those of standard PSO, traditional DE, and other algorithms. The results demonstrate the significant superiority of DE-PSO and WOAPSO. In 50 repeated experiments, the fitness standard deviation (STD) was significantly reduced, and the algorithms achieved rapid convergence by the mid-iteration stage, which effectively resolves the issues of premature convergence and local oscillation tendencies inherent in the standard Particle Swarm Optimization algorithm. Regarding the key parameters ($T_g$, ${\beta }_{max}$, $\gamma$) of the standard, the STD of the improved algorithm approached zero, verifying its strong adaptability to multimodal optimization problems. Furthermore, the DE-PSO algorithm had the best performance in balancing computational efficiency and stability, with a convergence speed that is three times faster than that of standard DE algorithm while maintaining the lowest parameter volatility. This study provides an efficient algorithmic tool for the rapid analysis of strong motion records and the efficient calibration of design response spectra, which has implications for the seismic optimization design of complex structures and can be guided by regulations, contributing to engineering seismic practice. Full article

The geometry of soft actuators significantly impacts their performance, including force generation, range of motion, and adaptability. Optimizing actuator geometry and material properties under specific constraints is crucial for achieving desired performance. This paper presents an optimization workflow employing metaheuristic algorithms in synergy with SolidWorks and Sorotoki, a newly developed MATLAB toolkit for soft robotics. The workflow optimizes actuator geometry to maximize bending while minimizing actuating pressure. A metaheuristic algorithm iteratively modifies the actuator’s design in SolidWorks, according to finite element analysis conducted using Sorotoki. To ensure accurate simulations, a uniaxial tensile test is performed on Thermoplastic Polyurethane (TPU), with curve fitting based on metaheuristic algorithms for precise hyperelastic modeling. The Ogden and Yeoh models are compared, with results indicating the Ogden model best represents TPU behavior. Four metaheuristic algorithms—Particle Swarm Optimization (PSO), Genetic Algorithm, Simulated Annealing, and Moth Flame Optimization (MFO)—are evaluated. PSO outperforms others in material modeling, while MFO yields the most effective actuator geometry. This workflow enables the design of more efficient and adaptable soft actuators for applications in robotics, prosthetics, and biomedical devices. Full article

In the context of large-scale renewable integration and increasing demand for power-system flexibility, energy-storage systems are indispensable components of modern grids, and their safe, reliable operation is a decisive factor in investment decisions. To mitigate lifecycle degradation and cost increases caused by frequent charge–discharge cycles, this study puts forward a two-layer energy storage capacity configuration optimization approach with explicit integration of cycle life restrictions. The upper-level model uses time-of-use pricing to economically dispatch storage, balancing power shortfalls while maximizing daily operational revenue. Based on the upper-level dispatch schedule, the lower-level model computes storage degradation and optimizes storage capacity as the decision variable to minimize degradation costs. Joint optimization of the two levels thus enhances overall storage operating efficiency. To overcome limitations of the conventional Whale Optimization Algorithm (WOA)—notably slow convergence, limited accuracy, and susceptibility to local optima—an Improved WOA (IWOA) is developed. IWOA integrates circular chaotic mapping for population initialization, a golden-sine search mechanism to improve the exploration–exploitation trade-off, and a Cauchy-mutation strategy to increase population diversity. Comparative tests against WOA, Gray Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO) show IWOA’s superior convergence speed and solution quality. A case study using measured load data from an industrial park in Zhuzhou City validates that the proposed approach significantly improves economic returns and alleviates capacity degradation. Full article

Modern machining industries require high precision and efficiency in machine tools, where thermal deformations significantly impact accuracy. This study focuses on optimizing the structural parameters of a vertical turning center to minimize thermal displacements affecting machining precision. The optimization process is divided into parametric and topological methodologies. The parametric approach targets three primary objectives: minimizing mass (q₁), maximizing static stiffness (q₂), and reducing thermal displacement (q₃). Multi-criteria optimization techniques, including Pareto-based and scalarization methods, are applied to balance these conflicting factors. Finite Element Analysis (FEA) models assist in evaluating machine stiffness and displacement, with constraints imposed on structural mass and stiffness to maintain performance. Parametric optimization, using iterative computational algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), refines rib and wall thicknesses of the lathe table to achieve displacement reductions. The optimization process successfully lowers displacement at critical measurement points while maintaining structural integrity. Hybrid PSO (hPSO) outperforms other algorithms in achieving optimal parameter sets with minimal computational effort. Topological optimization, based on the Solid Isotropic Microstructure with Penalization (SIMP) method, further enhances structural efficiency by refining material distribution. The iterative process identifies optimal energy flow paths while ensuring compliance with mechanical constraints. A hybrid approach integrating parametric adjustments with topological refinement leads to superior performance, achieving a 43% reduction in displacement at key measurement points compared to the initial design. The final optimized design reduces mass by 1 ton compared to the original model and 2.5 tons compared to the best rib–wall optimization results. The study’s findings establish a foundation for implementing active deformation compensation systems in machine tools, enhancing machining precision. The integration of parametric and topological optimization presents a robust framework for designing machine tool structures with improved thermal stability and structural efficiency. Full article

Additive manufacturing is driving a significant change in industry, extending beyond prototyping to the inclusion of printed parts in final designs. Stereolithography (SLA) is a polymerization technique valued for producing highly detailed parts with smooth surface finishes. This study presents a hybrid intelligent framework for modeling and optimizing the SLA 3D printer process’s parameters for Acrylonitrile Butadiene Styrene (ABS) photopolymer parts. The nonlinear relationships between the process’s parameters (Orientation, Lifting Speed, Lifting Distance, Exposure Time) and multiple performance characteristics (ultimate tensile strength, yield strength, modulus of elasticity, Shore D hardness, and surface roughness), which represent complex relationships, were investigated. A Taguchi design of the experiment with an L18 orthogonal array was employed as an efficient experimental design. A novel hybrid fuzzy logic–Particle Swarm Optimization (PSO) algorithm, ARGOS (Adaptive Rule Generation with Optimized Structure), was developed to automatically generate high-accuracy Mamdani-type fuzzy inference systems (FISs) from experimental data. The algorithm starts by customizing Modified Learn From Example (MLFE) to create an initial FIS. Subsequently, the generated FIS is tuned using PSO to develop and enhance predictive accuracy. The ARGOS models provided excellent performances, achieving correlation coefficients (R²) exceeding 0.9999 for all five output responses. Once the FISs were tuned, a multi-objective optimization was carried out based on the weighted sum method. This step helped to identify a well-balanced set of parameters that optimizes the key qualities of the printed parts, ensuring that the results are not just mathematically ideal, but also genuinely helpful for real-world manufacturing. The results showed that the proposed hybrid approach is a robust and highly accurate method for the modeling and multi-objective optimization of the SLA 3D process. Full article

Quadrupole mass spectrometers are highly sensitive and specific analytical instruments, widely used in pharmaceuticals, clinical diagnostics, and other fields. Their performance depends on a tuning process to optimize key parameters, which has traditionally relied on engineers’ expertise or simple univariate search methods. This paper proposes an automatic tuning method using an improved differential evolution algorithm. This algorithm introduces a ranking and subpopulation classification for individuals, enabling distinct mutation strategies. Validation on the CEC-2017 benchmark functions confirms the superiority of the improved algorithm. In automatic tuning experiments, it achieved a 25.3% performance gain over the univariate search method and also surpassed both the classical differential evolution algorithm and standard particle swarm optimization algorithm. This method proves to be an effective approach for enhancing the performance of quadrupole mass spectrometers. Full article

Particle Swarm Optimization (PSO) has been widely employed to optimize the deployment of Unmanned Aerial Vehicles (UAVs) in various scenarios, particularly because of its efficiency in handling both single and multi-objective optimization problems. In this paper, a framework for optimizing the deployment of edge-enabled UAVs using Pareto-PSO is proposed for data collection scenarios in which UAVs operate autonomously and execute onboard distributed multi-objective PSO to maximize the total non-overlapping coverage area while minimizing latency and energy consumption. Performance evaluation is conducted using key indicators, including convergence time, throughput, and total non-overlapping coverage area across bandwidth and swarm-size sweeps. Simulation results demonstrate that the Pareto-PSO consistently attains the highest throughput and the largest coverage envelope, while exhibiting moderate and scalable convergence times. These results highlight the advantage of treating the objectives as a vector-valued objective in Pareto-PSO for real-time, scalable, and energy-aware edge-UAV deployment in dynamic Internet of Flying Things environments. Full article

This study proposes a composite acoustic porous duct metamaterial muffler composed of a perforated tortuous channel and an externally wrapped porous layer, integrating both structural resonance and material damping effects. Theoretical models for the perforated plate, tortuous channel, and porous material were established, and analytical formulas for the total acoustic impedance and transmission loss of the composite structure were derived. Finite element simulations verified the accuracy of the models. A systematic parametric study was then performed on the effects of porous material type, thickness, and width on acoustic performance, showing that polyester fiber achieves the best results at a thickness of 30 mm and a width of 5 mm. Further analysis of periodic distribution modes revealed that axial periodic arrangement significantly enhances the peak noise attenuation, radial periodic arrangement broadens the effective bandwidth, and multi-frequency parallel configurations further expand the operating range. Considering practical duct conditions, a single-layer multi-cell array was constructed, and its modal excitation mechanism was clarified. By employing the Particle Swarm Optimization (PSO) algorithm for multi-parameter optimization, the average transmission loss was improved from 26.493 dB to 29.686 dB, corresponding to an increase of approximately 12.05%. Finally, physical samples were fabricated via 3D printing, and four-sensor impedance tube experiments confirmed good agreement among theoretical, numerical, and experimental results. The composite structure exhibited an average experimental transmission loss of 24.599 dB, outperforming the configuration without porous material. Overall, this work highlights substantial scientific and practical advances in sound energy dissipation mechanisms, structural optimization design, and engineering applicability, providing an effective approach for broadband and high-efficiency duct noise reduction. Full article

Urban water supply networks are prone to complex noise interference, which significantly degrades the performance of data-driven forecasting models. Conventional denoising techniques, such as standard Variational Mode Decomposition (VMD), often rely on empirical parameter selection or optimize only a subset of parameters, lacking a robust mechanism for identifying noise-dominant components post-decomposition. To address these issues, this paper proposed a novel denoising framework termed Dynamic Hyperbolic Tangent PSO-optimized VMD (DHTPSO-VMD). The DHTPSO algorithm adaptively adjusts inertia weights and cognitive/social learning factors during iteration, mitigating the local optima convergence typical of traditional PSO and enabling automated VMD parameter selection. Furthermore, a dual-criteria screening strategy based on Variance Contribution Rate (VCR) and Correlation Coefficient Metric (CCM) is employed to accurately identify and eliminate noise-related Intrinsic Mode Functions (IMFs). Validation using pressure data from District A in Zhejiang Province, China, demonstrated that the proposed DHTPSO-VMD method significantly outperforms benchmark approaches (PSO-VMD, EMD, SABO-VMD, GWO-VMD) in terms of Signal-to-Noise Ratio (SNR), Mean Absolute Error (MAE), and Mean Square Error (MSE). Subsequent forecasting experiments using an Informer model showed that signals preprocessed with DHTPSO-VMD achieved superior prediction accuracy (R² = 0.948924), underscoring its practical utility for smart water supply management. Full article