Search Results (4,184)

High-voltage electric-vehicle (EV) fast charging requires accurate coordination between the off-board charger and the battery management system during voltage and current negotiation. This study evaluates a Hybrid Particle Swarm Optimization-Whale Optimization Algorithm (HPWOA) schedule for training a dual-output feedforward neural network that directly estimates ChargePower_kW and ChargeCurrent_A. Ten protocol-state and battery-condition variables were used as inputs. The 158-dimensional neural-weight vector was optimized using 75 Particle Swarm Optimization (PSO) iterations, followed by 75 Whale Optimization Algorithm (WOA) iterations. Using the supplied 500-record dataset, a reproducible 30-seed sample-level evaluation was conducted with a common 3775 fitness-function-evaluation budget for PSO, the WOA, the SFSA, and the HPWOA. The Stochastic Fractal Search Algorithm (SFSA), therefore, used 30 iterations because it evaluates five diffusion candidates per individual. The reported HPWOA mean ± standard deviation (SD) was RMSE = 4.658 ± 0.986 kW and R² = 0.843 ± 0.071 for power, and RMSE = 12.686 ± 2.687 A and R² = 0.858 ± 0.059 for current. The HPWOA outperformed the WOA and SFSA, but not standalone PSO. A conventional mini-batch Adam-trained dual-output neural network produced RMSE = 1.704 ± 0.121 kW and 4.946 ± 0.273 A, and R² = 0.980 ± 0.003 and 0.979 ± 0.002, respectively. Charger-grouped five-fold validation gave the HPWOA R² = 0.857 ± 0.045 (power) and 0.873 ± 0.048 (current). An analytical P = V × I reconstruction was physically consistent in construction and did not show a statistically significant power–RMSE difference from direct HPWOA outputs. The results, therefore, position the two-phase schedule as a reproducible comparative baseline rather than as a demonstrated replacement for gradient-based training or a physically constrained reconstruction. Full article

Permanent Magnet Synchronous Motors (PMSMs) are widely employed in electric vehicle (EV) propulsion systems because of their high efficiency, high power density, and superior dynamic performance. The performance of field-oriented control (FOC)-based PMSM drives strongly depends on accurate tuning of the proportional–integral (PI) controllers governing the speed and current loops. Conventional tuning approaches often optimize a single performance index and therefore fail to simultaneously enhance the dynamic behavior of all control loops. This paper proposes a multi-error Integral of Time-weighted Absolute Error (ITAE)-based optimization framework for simultaneous tuning of the PI controllers by minimizing a composite objective function that incorporates the time-weighted absolute errors of the rotor speed, q-axis current, and d-axis current. To validate the effectiveness and optimizer independence of the proposed framework, five metaheuristic optimization algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), Gazelle Optimization Algorithm (GOA), and White Shark Optimization (WSO)—are evaluated under identical optimization settings. MATLAB/Simulink simulations are performed for reference-speed tracking, load disturbance rejection, and variable-speed operation. The results demonstrate that the proposed optimization framework consistently improves tracking accuracy and dynamic response regardless of the selected optimizer, while WSO provides the best overall performance. In the variable-speed tracking scenario, WSO achieved the lowest RMSE of 0.96 rad/s and the minimum ITAE value of 0.1716, confirming its effectiveness as the most suitable optimizer for the proposed framework in high-performance PMSM drive applications. Full article

Probabilistic slope stability analysis requires tools that are both computationally efficient and accurate for uncertainty propagation. This study develops a reliability-oriented surrogate framework coupling a multilayer perceptron artificial neural network with particle swarm optimization (ANN–MLP–PSO). The model was trained on 2014 homogeneous slope cases drawn from literature records and mechanics-based simulations. PSO identified a best-performing six-hidden-layer architecture achieving a coefficient of determination of R² = 0.95 on the held-out test set. The trained surrogate was embedded in a probabilistic sampling framework to estimate the probability of failure (P_f), reliability index (β), and factor-of-safety quantiles, then applied to the Mansourieh slope near Beirut, Lebanon, under dry and wet conditions. Outputs were benchmarked against the First-Order Second-Moment method (FOSM), the Point Estimate Method (PEM), and Monte Carlo simulation (MCS). The comparison showed that the ANN–MLP–PSO surrogate reproduced the dry-to-wet changes in factor-of-safety distributions, probability of failure, and reliability index obtained from the conventional reliability methods under the same probabilistic assumptions, with wet-scenario failure probabilities ranging from approximately 86% to 99%. Despite quantitative differences, all four methods identified the same reliability trend and engineering interpretation. Once trained, the surrogate enabled rapid probabilistic evaluation without repeated deterministic calculations, providing an efficient tool for slope stability screening and uncertainty-aware geotechnical decision support. Full article

Traditional particle swarm optimization (PSO) easily falls into premature convergence, while differential evolution (DE) is highly sensitive to fixed control parameters. Existing PSO-DE hybrid frameworks suffer from static population sizes and insufficient cross-population information exchange. This paper proposes PSO-DE-ADP, a hybrid optimizer with sinusoidal adaptive parameters, elite-guided mutation, ring neighborhood-weighted PSO and fitness-driven dynamic dual-population allocation. Four complementary mechanisms are integrated: (i) sine-wave perturbation superimposed on linear decay adaptively adjusts PSO inertia weight, acceleration factors and DE scaling/crossover coefficients to balance search stages; (ii) global elite individuals are embedded into DE mutation to reduce blind random search; (iii) ring topology with weighted learning realizes bidirectional information interaction between PSO and DE subpopulations; (iv) the proportion of PSO/DE individuals is dynamically adjusted according to elite ratio to allocate computing resources. Experiments adopt the CEC2017 30-dimensional benchmark with 30 test functions covering unimodal, multimodal, hybrid and composite landscapes. Compared with 8 state-of-the-art metaheuristics, PSO-DE-ADP achieves the lowest Friedman rank (1.08 vs. 2.23–4.90 for PSO variants; 1.53 vs. 2.07–5.00 for non-PSO algorithms). Ablation tests prove each component significantly boosts accuracy; The algorithm only costs 0.172 s average runtime, superior to all competitors. Statistical Wilcoxon and Friedman tests verify its significant superiority. Future work extends this method to multi-objective, constrained and real engineering optimization tasks. Full article

This paper presents a study on the prediction of greenhouse temperature and humidity using an improved particle swarm optimization (IPSO) algorithm combined with a long short-term memory (LSTM) neural network, namely IPSO-LSTM. Accurate environmental prediction is crucial for modern protected agriculture, but traditional LSTM models often suffer from suboptimal hyperparameter tuning. To address this, we propose an IPSO-LSTM model where an improved PSO with a linearly decreasing inertia weight is employed to automatically search for the optimal hyperparameters of the LSTM. Experimental results based on hourly data collected from a Venlo-type glass greenhouse over a 90-day period demonstrate the superiority of the proposed model. Specifically, in the tomato scenario, the IPSO-LSTM achieved an R² of 0.9969 for temperature and 0.9967 for humidity, with MAPE values as low as 0.0118 and 0.0127, respectively. These results indicate that the IPSO-LSTM model significantly outperforms conventional LSTM and standard PSO-LSTM models, providing a reliable tool for intelligent greenhouse climate control. Full article

Desert highways have long been subjected to aeolian sand hazards, and sand accumulation on the pavement significantly weakens the surface texture and deteriorates skid resistance, which has become one of the core contributing factors to traffic accidents on desert road sections. Current research predominantly focuses on the attenuation law of the macroscopic friction coefficient of sand-covered pavements; however, the quantitative correlation mechanism between three-dimensional micro-texture characteristics and skid resistance has not been sufficiently revealed, and there is a lack of high-precision skid resistance prediction methods under multi-condition coupling scenarios. To address the above research deficiencies, this paper takes the asphalt pavement in the Tengger Desert region as the research object. A handheld three-dimensional texture scanning system was employed to acquire the three-dimensional pavement morphology parameters under different sand coverages, and the sideway force coefficient (SFC) was synchronously measured under the corresponding conditions. Through Pearson correlation analysis and dual multiple comparison correction using the FDR-BH and Bonferroni methods, the core influencing indicators were identified. Subsequently, a skid resistance prediction model based on a BP neural network optimized by the particle swarm optimization (PSO) algorithm was constructed and horizontally compared and validated with LSTM and PSO-SVM models. The research results show the following: ① under dry conditions, the root mean square height ($S_q$), peak density ($S_{pd}$), arithmetic mean peak curvature ($S_{pc}$), valley void volume ($V_{vv}$), root mean square slope ($S_{dq}$), and developed interfacial area ratio ($S_{dr}$) are significantly linearly correlated with the SFC, among which $S_q$, $S_{pd}$, $S_{pc}$, and $V_{vv}$ are the core controlling indicators, with the absolute values of their correlation coefficients all exceeding 0.73, and ② the constructed PSO-BP prediction model achieved a coefficient of determination $R^2$ of 0.86093 on the test set, and its prediction accuracy and generalization ability are both superior to those of the LSTM and PSO-SVM models, enabling it to effectively characterize the nonlinear mapping relationship between multiple texture parameters and skid resistance. This study can provide theoretical support and a technical basis for skid resistance evaluation, sand accumulation disaster warning, and scientific maintenance decision-making for desert highways. Full article

The three-dimensional single bin packing problem with categorical compatibility constraints (3D-SBPP-CC) extends the classical NP-hard packing problem by adding pairwise incompatibility rules over item categories. Although such constraints arise in hazardous materials logistics, no prior study has provided a complete mathematical formulation or a systematic multi-algorithm benchmark for this problem. This paper presents a novel mixed-integer linear programming (MILP) formulation of the 3D-SBPP-CC together with a strong NP-hardness proof. Four population-based metaheuristics—genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and differential evolution (DE)—are each implemented with two constraint-handling strategies (penalty function and repair operator), yielding eight configurations. These are evaluated against three baselines under a uniform budget of 10,000 fitness evaluations. Experiments on 45 stratified benchmark instances with 10 independent runs per configuration (4950 total runs) produce three findings. First, the eight metaheuristic variants and random search form a statistically homogeneous tier (Friedman ${\chi }^2=197.49$, $p<{10}^{-36}$; $CD=2.21$). Second, constraint density strongly moderates algorithm ranking (Kendall’s W: 0.69 → 0.08). Third, decoder-embedded compatibility filtering renders explicit repair and penalty strategies equivalent ($p>0.05$ at all constraint levels). The greedy heuristic achieves 84.2% of the best metaheuristic’s utilization in under 0.1 s; random search reaches 98.7%. These results demonstrate that the DBLF placement decoder, not the choice of metaheuristic, governs packing quality for this problem class. Full article

Cardiovascular Disease (CVD) is a prevailing issue across the world. It requires an accurate method for diagnosing in order to treat the disease effectively. The use of machine learning (ML) techniques has gained popularity for diagnosing CVDs. Some existing ML models have demonstrated good results using the process of manual hyperparameter tuning. But manual hyperparameter tuning is a very tedious task that may cause fluctuations in diagnosis accuracy. To construct a PSO-tuned classifier that provides stable diagnosis for CVD, this paper proposes a robust hybrid framework named PSO-PNN using a particle swarm optimization algorithm for fine-tuning the value of the Smoothing Parameter ($\sigma$) of a probabilistic neural network (PNN). PSO-PNN could be employed as an effective classifier for cardiovascular disease. To validate the effectiveness of PSO-PNN, a dataset obtained from UCI Cleveland database was utilized containing 303 patients’ data. The results indicated that the proposed PSO-PNN framework improved the baseline PNN performance; obtained an accuracy of 91.3%, an ROC-AUC value of 94.8%, and a recall rate of 95.24%; and showed stable predictive performance across 30 independent PSO executions. Additionally, SHAP was applied as a post hoc explainability method to interpret the trained PSO-PNN predictions and identify the contribution of input features. The proposed framework may support future decision-support applications for CVD prediction; however, further external validation is required before practical clinical use can be considered. Full article

Adequate soil quality is essential for ensuring the productivity and sustainability of agriculture, with soil pH being a key variable due to its influence on nutrient availability, microbial activity, and plant development. This study proposes a Mamdani fuzzy inference system (FIS) optimized through metaheuristic algorithms for soil pH prediction in papaya (Carica papaya L.) cultivation, a crop highly sensitive to pH fluctuations within the rhizosphere. Soil temperature and soil moisture were used as independent variables, while the estimated soil pH constituted the dependent variable of the system. Three optimization techniques—genetic algorithms (GAs), Differential Evolution (DE), and Particle Swarm Optimization (PSO)—were evaluated to optimize the membership functions and fuzzy rule base of the Mamdani FIS. Model performance was assessed through 30 independent runs using 1500 records collected from a commercial papaya plantation. Across the 30 independent runs, the GA-optimized model achieved the best overall predictive performance, with a Mean Absolute Error (MAE) of 0.3636 ± 0.0035, Mean Relative Error (MRE) of 0.0554 ± 0.0004, and mean coefficient of determination (r²) of 0.8699 ± 0.0048. The best observed GA values were an MAE of 0.3305, MRE of 0.0513, and r² of 0.8957. In addition, a web-based decision support platform and an automated Telegram alert system were developed for event-driven pH alert notification. The results confirm that GA-optimized fuzzy systems constitute an effective, interpretable, and practical tool for pH-based soil condition monitoring and agronomic decision support in precision agriculture. Full article

Modern power systems are increasingly exposed to uncertainties and face rising demands for operational resilience. To address this challenge, this paper investigates the optimal placement of thyristor-controlled series compensation (TCSC) devices within flexible AC transmission systems (FACTS). A comprehensive vulnerability evaluation index is developed by integrating network structure, load impact, and branch disconnection factors, enabling a holistic identification of vulnerable transmission links. Subsequently, a multi-objective TCSC optimization model is formulated to simultaneously minimize the system-wide comprehensive vulnerability index and the total investment cost. To solve this model, an improved multi-objective particle swarm optimization (MOPSO) algorithm is devised, incorporating chaotic initialization and adaptive inertia weight adjustment to enhance both global exploration and local exploitation capabilities. The proposed method is validated using the IEEE 39-bus and IEEE 118-bus test systems. The results demonstrate that the optimized placement significantly reduces system vulnerability, maintains a favorable economic balance and improves the system security margin. Furthermore, uncertainty tests involving load variations, line parameter perturbations, and wind power fluctuations, as well as malicious attacks, confirm the robustness of the proposed placement strategy. This work provides a practical and effective framework for resilience-oriented TCSC planning, contributing to mitigating cascading failure risks and enhancing power system security. Full article

Sustainable manufacturing increasingly demands environmentally friendly machining strategies, and dry machining has become recognized as a sustainable alternative to conventional coolant-assisted processes. This study presents a framework built on a machine learning technique for optimizing the dry machining performance of Super Duplex Stainless Steel (SDSS 2507) using textured cutting inserts. Gaussian process regression (GPR) models were developed to predict maximum roughness depth (R_max) and maximum flank wear (VB_max). Gaussian data augmentation was employed to enhance model generalization. The predictive performance was strong, with R² values recorded above 0.95 on testing datasets. To identify optimal machining parameters, GPR was integrated with particle swarm optimization (PSO), enabling independent optimization of R_max and VB_max. The framework achieved reductions of 13.97% in R_max and 30.70% in VB_max compared to experimental benchmarks. The results confirm the effectiveness of data-driven optimization in enhancing surface quality, tool performance, and intelligent machining control. Full article

High penetration levels of renewable energy and power electronic apparatus create prominent obstacles for novel power grids, which mainly manifested as inadequate system inertia and a deteriorated stability margin. To overcome such drawbacks, this research develops an operational control method to maintain safe and steady grid operation with large-capacity grid-forming energy storage connected to the system. This paper first builds a dynamic voltage model covering grid-forming energy storage, distributed renewable generators, and distribution network frameworks. Then it explores how different control parameter settings of grid-forming storage affect dynamic voltage regulation capabilities under distinct R-L ratio scenarios. Since the correlation between energy storage control variables and voltage regulation features is highly nonlinear and complicated, trajectory sensitivity analysis is adopted to linearize these coupling constraints, which are further embedded into the power system security operation mathematical model. A chaotic particle swarm optimization (PSO) algorithm is used to solve the constructed optimization model. Simulation tests on a modified IEEE 33-bus test system ultimately prove that the proposed method is reliable and practically applicable. Simulation results on the modified IEEE 33-bus test system demonstrate that the proposed strategy restricts grid voltage fluctuation rate to only 2.41%, raises renewable energy accommodation rate up to 98.4%, and achieves a 30.6% reduction in overall system operation cost compared to traditional energy storage configuration schemes, which fully verifies the outstanding effectiveness and practical engineering feasibility of the proposed method. Full article

Classification is a fundamental technique in data mining that predicts categorical labels by analyzing input features. However, training Artificial Neural Networks (ANNs) using traditional methods often encounters challenges, such as getting stuck in local minima and slow convergence. To address these issues, this study proposes a novel hybrid model, IPA-ANN, which integrates the Immune Plasma Algorithm (IPA) to optimize the ANN’s connection weights and biases. The IPA, inspired by the immune plasma treatment process, utilizes a unique donor-receiver mechanism to balance exploration and exploitation in the search space. The proposed model was evaluated on nine benchmark datasets from the UCI repository and compared with 18 state-of-the-art metaheuristic algorithms, including Grey Wolf Optimization (GWO), Differential Evolution (DE), and Particle Swarm Optimization (PSO). Experimental results were analyzed using accuracy, F1-score, confusion matrices, and convergence graphs. The findings indicate that IPA-ANN achieves competitive and stable classification performance across different datasets while demonstrating favorable convergence characteristics in several cases. Furthermore, the study investigates the influence of donor–receiver parameters on the optimization process, highlighting the adaptability of the proposed framework. The reliability of these findings was further examined through repeated stratified 5-fold cross-validation and paired Wilcoxon signed-rank tests with Holm–Bonferroni correction on representative datasets, confirming that a subset of the observed performance differences are statistically significant, and through a computational cost analysis showing that IPA-ANN incurs no additional overhead relative to the majority of the compared algorithms. This study contributes to the literature by presenting the first documented application of IPA in ANN training and by providing a modular infrastructure for future metaheuristic-based ANN optimization studies. Full article

The large-scale integration of photovoltaic generation into distribution grids has introduced significant operational challenges, including voltage excursions, reverse power flows, and increased variability. Battery energy storage systems (BESSs) offer a versatile solution by providing coordinated active- and reactive-power support. However, their scheduling in active distribution networks is challenging because of the non-convex alternating-current (AC) power-flow equations, the nondifferentiability of battery-degradation modeling, and uncertainty in renewable generation and demand. This paper proposes a two-stage methodology for the day-ahead operation of BESSs in ADNs. In the first stage, parallel particle swarm optimization (PPSO) determines the hourly active- and reactive-power schedules of the BESS units. In the second stage, a matrix-based multi-period AC power flow based on successive approximations evaluates the schedules and verifies voltage, thermal, converter-capability, and state-of-charge (SoC) constraints. A rainflow-counting degradation model is incorporated into the objective function to account for cycling and calendar aging costs. The methodology is assessed through ablation analyses comparing active-power-only and coordinated P–Q dispatches, degradation-unaware and degradation-aware scheduling, and serial and parallel PSO implementations. It is validated on modified 33-, 69-, and 136-node systems under deterministic and uncertainty-based operating conditions, including 100 demand and PV-generation scenarios. PPSO is compared with parallel versions of the adaptive Jaya algorithm (AJAYA), genetic algorithm (GA), multi-verse optimizer (MVO), salp swarm algorithm (SSA), grey wolf optimizer (GWO), and vortex search algorithm (VSA), using operating-cost reduction, computational time, solution variability, feasibility indicators, BESS lifetime, and weekly cost analysis. Additionally, exact one-sided Wilcoxon signed-rank tests with Holm adjustment are used to assess the statistical significance of the economic differences between PPSO and the benchmark methods. Results show that PPSO provides the lowest or most competitive operating costs and the shortest computational time in the evaluated cases, while all network and storage constraints remain satisfied. Full article

Manufacturing errors introduce phase deviations in Mach–Zehnder interferometers (MZIs) that degrade the fidelity of optical processors. To address this issue, we employ a Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton method for phase calibration of a $4\times 4$ reconfigurable MZI optical processor based on the Reck architecture. By optimizing the mapping from the target matrix to the optical network, the method determines the optimized phase parameters of 12 phase shifters. Thermo-optic simulations are further used to establish the relationship between the applied bias voltage and the induced phase shift, providing a link between the optimized phase parameters and the electrical driving conditions. Compared with Particle Swarm Optimization (PSO), Genetic Algorithms (GA), and Gradient Descent with Momentum (GDM), the BFGS method provides faster convergence and high calibration fidelity. These results demonstrate an efficient approach for phase calibration of programmable MZI optical processors. Full article