Search Results (7,032)

In multi-AUV collaborative underwater coverage operations, the fundamental principle of load balancing is temporally rather than spatially defined. Traditional partition schemes that equalize area or grid counts suffer from a critical fallacy: they assume a linear mapping from geometry to working time, which collapses in the presence of irregular obstacles. This paper demonstrates that temporal load imbalance causes some AUVs to finish prematurely and remain idle—consuming power and fighting currents while their counterparts struggle with topologically complex sub-regions. To address this, we propose a dual-layer nested Particle Swarm Optimization (PSO) framework for Voronoi partitioning, where the outer layer coarsely initializes grid counts, while the inner layer directly minimizes the makespan and time variance derived from actual BINN-simulated coverage paths. This two-stage PSO architecture effectively resolves the nonlinear mismatch between geometric partitioning and real operation time, redefining load balancing from a geometrical abstraction to a temporally grounded, operationally relevant metric. Furthermore, we rigorously distinguish the intrinsic nature of coverage path planning (CPP) from Traveling Salesman Problem (TSP)-based point routing—the latter generates discontinuous, sharp-turning trajectories that violate the kinematic constraints of side-scan sonar payloads and cause critical data gaps. The proposed architecture yields a standard deviation of mission times that is an order of magnitude lower than area-based heuristics, while maintaining kinematically feasible continuous sweeps. Crucially, we explicitly delineate the operational boundary of this framework: it is purpose-built for static, pre-surveyed environments where offline computational overhead (approximately 8 min) is a justifiable investment against a 3 h optimal field execution. Full article

This paper proposes a two-stage, reliability-driven multi-objective planning framework for fast electric vehicle charging stations (FCSs) integrated with solar photovoltaic (PV) generation and battery energy storage systems (BESS) in coupled power–transportation networks. The framework simultaneously addresses electrical network constraints, transportation-driven charging demand, and techno-economic trade-offs in high EV penetration scenarios. A benchmark IEEE 69-bus radial distribution system is co-simulated with a 25-node transportation network to realistically capture spatial and temporal interactions between EV mobility and grid operation. To quantify the combined impacts of voltage stability, service continuity, and charging uncertainty, a novel average voltage deviation reliability index (AVDRI) is introduced. Spatially and temporally varying EV charging demand is modeled using a hybrid approach that integrates queuing theory with gravity-based traffic interaction models, enabling a realistic representation of stochastic arrival patterns and route-dependent charging behavior. In the first stage, a multi-objective optimization problem is formulated to determine the optimal locations and charging capacities of FCSs, minimizing system power losses, reliability degradation, and total system cost while maximizing EV serviceability. Multi-objective particle swarm optimization (MOPSO), multi-objective grey wolf optimization (MOGWO), and a proposed hybrid GWOPSO algorithm are comparatively evaluated. In the second stage, a bisection-based sizing strategy is employed to determine the optimal PV and BESS capacities required to mitigate solar intermittency and peak power generation mismatches. Results demonstrate that the proposed hybrid GWOPSO based framework achieves superior convergence characteristics and delivers significant improvements in voltage profile, reliability indices, power loss reduction, and overall techno-economic performance compared to conventional approaches. Full article

New-type infrastructure (NI) may increase carbon emissions through scale expansion while reducing them through technological progress, but systematic empirical testing of these competing effects remains limited. Using panel data for 30 Chinese provinces from 2013 to 2024, this study examines the nonlinear effects of NI on carbon emissions within a two-way fixed-effects framework, and employs the system generalized method of moments (GMM) and instrumental variable approaches to address potential endogeneity arising from dynamic panel bias, omitted variables, and other sources. We then examine industrial structure rationalization and green technological innovation as mediating variables, estimate spatial dependence with a spatial Durbin model, and combine particle swarm optimization–support vector machine (PSO-SVM) forecasting with alternative scenario assumptions. The evidence supports an inverted U-shaped association between NI and carbon emissions. Both mediating variables are significant, but the nonlinear relationship is found only in the central and western regions; it is not significant in the eastern or northeastern regions. NI also has significant cross-regional spatial spillover effects. Across the four scenarios, emissions ultimately decline, the green-transition scenario reaches the earliest and lowest peak, and rapid NI development produces greater pressure in the near term. These results characterize NI as a stage-dependent and spatially connected driver of emissions and support differentiated regional implementation of the Digital China and Beautiful China strategies. Full article

Natural diversion channels offer a promising solution for flood mitigation within the context of natural flood management; however, their design must balance hydraulic performance and intervention scale. This study presents an integrated framework for the optimal design of modified natural diversion channels by coupling two-dimensional hydrodynamic modelling with surrogate modelling and multi-objective optimisation. A Hazard Estimation Index (HEI) is derived from 2D flood hazard maps by aggregating the spatial distribution of flood hazard within the township and agricultural floodplain areas, providing a quantitative measure in which higher values indicate greater flood hazard. Multiple Linear Regression (MLR) models are developed to approximate HEI as a function of flood peak discharge and channel cross-sectional area. Separate HEI formulations are defined for township and floodplain areas to reflect differing mitigation priorities. The surrogate models are integrated within a Multi-Objective Particle Swarm Optimisation (MOPSO) framework to minimise flood hazard in both domains and channel size, with channel cross-sectional area used as a geometric proxy for excavation cost rather than as a direct estimate of construction cost. Three independent optimisation systems are developed for minor-to-moderate, major, and very major flood regimes based on flood-frequency analysis. Application to Moree Plains, Australia, shows that minor-to-moderate floods can be addressed with an optimal channel size of approximately 425 m², achieving HEI values of approximately 0.18 in the township and 0.07 in the floodplain. For major floods, a larger channel of approximately 1016 m² results in HEI values of approximately 0.35 and 0.26, respectively. For very major floods, a substantially larger channel of approximately 1620 m² is required, while township HEI remains relatively high (>0.5), indicating substantial residual risk. The results demonstrate diminishing hazard-reduction benefits with increasing channel size for larger floods and highlight the value of a regime-specific optimisation approach for supporting risk-informed natural flood management planning. Full article

This study presents an innovative, integrated control architecture combining adaptive online linear quadratic integral (AOLQI) and Stanley geometric control systems for autonomous vehicle path tracking. A key feature of the proposed framework is the offline optimization of the AOLQI parameters using the Particle Swarm Optimization (PSO) algorithm. To address varying road conditions, a Forgetting Factor Recursive Least Squares (FFRLS) algorithm is employed for real-time tire cornering stiffness estimation, complemented by a Kalman–Bucy filter for high-fidelity vehicle side-slip angle observation. The efficacy of this architecture is validated through MATLAB/Simulink and IPG CarMaker co-simulations across demanding benchmarks, including a 100-m radius circular path, the high-speed Hockenheim race track and the high-curvature Stelvio Pass road profile. Numerical evaluations demonstrate that, compared to the conventional LQI, the proposed approach achieves reductions in root mean square error (RMSE) of 44.85%, 24.76% and 51%, and decreases in integral square error (ISE) of 69.2%, 43.16% and 75.86% respectively, in these different scenarios. These results confirm that using an integrated approach with adaptive online LQI and Stanley control, alongside an estimation layer, ensures superior tracking precision and performance improvements across extreme road geometries. Full article

Beamforming for linear antenna arrays (LAAs) has become a critical research topic in advanced 5G and emerging 6G wireless communication systems due to the increasing demand for high spectral efficiency, interference mitigation, and enhanced radiation performance. This paper proposes an enhanced hybrid optimization framework based on Harris Hawks Optimization integrated with the Covariance Matrix Adaptation Evolution Strategy (HHO–CMA-ES) for unified LAA synthesis. The proposed approach simultaneously optimizes excitation amplitudes, phase shifts, and inter-element positions to achieve substantial peak sidelobe level (PSLL) reduction while preserving desirable beam characteristics. The optimization performance of the proposed method is systematically evaluated against several widely used metaheuristic algorithms, including the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Flower Pollination Algorithm (FPA), and conventional Harris Hawks Optimization (HHO), under identical simulation conditions. Simulation results obtained for a 20-element LAA demonstrate the superior effectiveness of the proposed HHO–CMA-ES framework across multiple optimization scenarios. In amplitude-only optimization, the proposed method achieves a PSLL of −41.746 dB, corresponding to an improvement of approximately 29.3% compared with WOA and more than 88% relative to GA. For amplitude–phase optimization, HHO–CMA-ES improves PSLL by nearly 21% compared with WOA. In the amplitude–position scenario, the proposed approach achieves the best performance with a PSLL of −44.54 dB, yielding an approximately 63% improvement over PSO and more than 111% over GA. Furthermore, the proposed framework exhibits faster convergence, improved solution stability, and enhanced beamforming robustness, confirming its suitability for high-dimensional antenna synthesis problems in future 5G/6G communication systems. Full article

Torque characteristic surfaces in nonlinear hydraulic turbine models are typically derived from model tests. However, model–prototype discrepancies can reduce the accuracy of dynamic simulations of hydropower units. To improve hydraulic turbine modeling accuracy, this study uses measured operational data to perform a multi-stage correction of the torque characteristic surface. The method calibrates the input–output mapping of the original surface through sequential parameter estimation, with parameters fixed after each stage. Polynomial, Gaussian kernel and Sigmoid functions are combined with six port sequences to construct 18 correction schemes, with the parameters at each stage optimized using particle swarm optimization. The results show that correction accuracy and the preferred sequence depend on the function form. For the studied unit, the Gaussian kernel with the “guide-vane opening–unit torque–unit speed” sequence yields the lowest weighted composite error, reducing it by 80.76% relative to the original model. The corrected data in the normal operating region are further used to construct the zero-opening and zero-unit-speed boundaries, which are combined with the runaway-speed boundary to reconstruct the full-operating-range torque characteristic surface using a backpropagation neural network (BPNN). The resulting NRMSE and NMaxAE are 0.54% and 1.58%, respectively. The corrected model is then embedded in the hydropower unit for multi-condition validation under primary frequency regulation. The mean RMSE and MAE of active power decrease by 45.10% and 50.46%, respectively, while the mean accuracy of the response regulation magnitude increases to 99.27%. The prediction error of guide-vane opening is also reduced. These results demonstrate that the proposed method effectively reduces model–prototype discrepancies and improves the accuracy of dynamic prediction under primary frequency regulation. Full article

Vibration-based diagnosis can identify transformer core looseness non-intrusively, but subtle vibration differences and noise-disturbed Gramian angular field (GAF) textures reduce diagnostic accuracy at low signal-to-noise ratios (SNRs). This paper proposes a variational mode decomposition (VMD)-assisted dual-representation cross-interaction network (DGCM-Net) with multi-SNR training. VMD parameters are optimized by particle swarm optimization (PSO), and signals reconstructed from selected modes are encoded as paired Gramian angular summation field (GASF) and Gramian angular difference field (GADF) images. Two independent DenseNet121 branches extract features from the two representations. CrossGAF enables cross-branch interaction before fusion, while the residual bottleneck multi-scale selective kernel (RBMSSK) module processes fused features at different scales. Across five independent runs, the proposed method achieves 99.84 ± 0.11% accuracy under the standard condition and 97.39 ± 0.23% mean accuracy across four noisy test sets, with 91.32 ± 0.62% accuracy at 10 dB. On a public 50 kVA transformer dataset, retraining yields 98.20 ± 0.65% accuracy at 2.5 dB. In cross-transformer few-shot adaptation, using 5% of the target-domain training samples increases the mean accuracy across four noisy conditions from 93.63% to 97.16% over target-only training. Full article

Background: When a small design-of-experiments dataset is used to optimize a machining process with particle swarm optimization (PSO), the regression model chosen as the fitness function is rarely validated against more than one candidate response, checked against alternative model families, or checked for the optimization that follows from using the same cross-validation score to both tune and report a model. Methods: We revisit 24 end-milling trials on AISI 1045 steel (fifteen one-factor-at-a-time runs plus a Taguchi L9 array; every value was independently verified against the underlying thesis records) in which cutting force, cutting-zone temperature, and X-ray diffraction residual stress were measured. A multiple linear regression (MLR), a quadratic response-surface model (RSM), a support vector regression (SVR), and a Gaussian process regression (GPR) were each cross-validated by nested leave-one-out cross-validation against every response, with bootstrap 95% confidence intervals and paired bootstrap significance tests on the resulting R² values, as well as a variance-inflation-factor check on the combined design matrix. The best-validated model per response was then used inside an identical constricted-PSO routine, which was run across 30 random seeds per fitness function to assess the convergence stability. Results: Design collinearity was not a concern (all VIF ≤ 1.28). Under the nested validation, the response-specific pattern held for temperature and residual stress but not for cutting force: the SVR was the only model to beat the MLR by a margin that survived a paired bootstrap test (residual stress, P (SVR not better) = 0.02); for force, a quadratic RSM model outperformed all three other families (R² = 0.72 vs. 0.39 for MLR and 0.28 for GPR), and the GPR’s earlier apparent advantage for force did not survive the leakage-free hyperparameter selection. The residual-stress PSO result was stable across seeds: 27 of 30 of the SVR-fitness runs converged to the same interior optimum (579.8 rpm, 40 mm/min, 0.413 mm; mean −472.5 MPa, SD 3.4 MPa), against a boundary optimum found deterministically by every MLR-fitness run (355 rpm, 40 mm/min, 0.5 mm, −434.6 MPa). The validated interior optimum also predicted a 55% lower cutting force and a slightly lower temperature at the same operating point. Conclusions: Whether a nonlinear surrogate should replace a linear one is response-specific and must be checked with a leakage-free validation scheme rather than assumed; for this dataset, that check overturns the original force-model recommendation while confirming the residual-stress result under both cross-model and multi-seed checks. The residual-stress optimum remains a model prediction pending physical confirmation. Full article

Hybrid excitation generators offer high power density and adjustable magnetic field, making them attractive for power generation applications with strict volume and weight constraints. However, under strong field excitation conditions, deep saturation of the iron core leads to a strongly nonlinear relationship between the resultant air-gap flux linkage and the field current, causing traditional linear models to exhibit large errors in the saturation region. To address this issue, this paper proposes a piecewise nonlinear function-based method for fitting magnetic saturation characteristics. The function’s nonlinear trend is exploited to construct an analytical model that describes the flux–current relationship in both the linear and deep saturation regions. The unknown model parameters are then determined by solving an optimization problem that minimizes the sum of squared output voltage errors. Particle swarm optimization (PSO) is employed for global search, overcoming the challenges of initial-value dependence and local optima in such multimodal parameter spaces. Experimental data from a hybrid excitation generator are used as samples for validation. The results show that the nonlinear model optimized by PSO accurately fits the flux linkage variation over the full current range, reducing the error from 7.78% to approximately 1%. The proposed model is concise in form, requires low computational effort, and can be directly used as an accurate analytical method for performance analysis of hybrid excitation generators, demonstrating good engineering application value. Full article

Post-contingency corrective dispatch restores a feasible AC voltage–current state, but the corrected state is usually not carried forward to responsibility allocation. This paper proposes a matrix state-to-responsibility mapping (MSRM) model that maps the corrected AC state to network use, loss allocation, regional marginal cost, and line-level carbon responsibility indices. The model treats the corrected post-contingency state as a common attribution basis, so feasibility recovery and responsibility assessment are linked within the same state-dependent mapping. Corrective dispatch is formulated in rectangular coordinates as a constrained optimization problem that minimizes generation cost and controllable redispatch amount under current balance, generator output, voltage, and line capacity constraints. The resulting voltage–current solution is used to build generator-side and load-side contribution matrices, from which settlement quantities and line-level responsibility are obtained through a unified matrix sequence. Carbon responsibility is calculated after redispatch from the generator-side traced flow matrix and generator carbon intensity matrix; it is not imposed as a dispatch objective. A feasibility-guided adaptive particle swarm optimization method is adopted to obtain the corrected state. Reported data from a practical 58-bus Taipower 345 kV transmission system are used for validation. In the line outage and generator outage cases, maximum line loading is reduced from 110% to 98.973% and from 103.99% to 98.99%, respectively. The positive carbon responsibility of Line 36 decreases from 91.72 to 88.80 tCO₂/h, whereas that of Line 80 remains nearly unchanged despite wider redispatch. In the two tested contingency cases, feasibility recovery, settlement quantities, total emissions, and line-level responsibility change in different directions after corrective redispatch, indicating that these indices should be evaluated separately under stressed post-contingency operation. Full article

A surrogate-assisted inverse design framework is presented for selecting the power-law index of clamped–clamped axially functionally graded (AFG) Timoshenko pipes conveying fluid. The GITT model, 336-sample database, and trained MLP forward surrogate were developed in our previous study; the present contribution begins with the formulation and solution of the inverse problem. Millisecond-speed MLP inference is embedded in grid, particle swarm optimization (PSO), and genetic algorithm (GA) searches for prescribed modal-frequency and deflection targets. Single-variable, two-variable, weighted-sum, constrained, and Pareto formulations are examined. Continuous candidates from the single-variable cases are subjected to GITT-database interpolation verification, which is explicitly distinguished from a new independent GITT calculation. The feasible single- and dual-modal examples produce small database-interpolated target residuals, whereas an intentionally unattainable triplet case retains an approximately 16% fundamental-frequency residual and demonstrates the need for feasibility screening. The two-variable maps reveal non-unique parameter couplings and are treated as exploratory surrogate results unless the complete candidate coincides with, or is independently assessed against, the available database. A five-network ensemble assesses repeatability with respect to training-data partitioning only, while a sensitivity analysis connects the selected indices to the high-sensitivity gradation range. In the synchronized Case A benchmark, the online MLP grid and PSO searches require approximately 0.044 and 0.210 s, respectively, excluding the inherited offline GITT-database construction cost. Full article

Aiming at the problems of high energy consumption, high carbon emissions from data centers and the difficulty of renewable energy accommodation in distribution networks driven by rapid growth in computing tasks, this paper proposes a collaborative trading method for data center–power grid–energy storage systems to improve spatiotemporal flexibility. Firstly, an integrated mechanism model including IT equipment, HVAC cooling systems, delay-tolerant batch tasks and UPS energy storage is established to quantify multi-dimensional internal flexible regulation potential. Secondly, an improved k-means algorithm is adopted for scenario reduction of wind–PV outputs, and a stochastic-robust collaborative trading optimization model considering carbon emission cost is constructed. Multiple practical constraints are incorporated, including power balance, power flow limits, nodal voltage bounds, task service latency and state of charge limits of energy storage. An improved particle swarm optimization with premature-convergence indicator is developed to solve this nonlinear, non-convex, mixed-variable problem. Simulations are carried out on a modified IEEE 33-node test system over a 24 h scheduling horizon. Numerical results demonstrate that compared with the conventional demand-response strategy, the proposed method reduces total operational cost by 10.7%, curtails wind–PV abandoned power, and achieves 28.6% peak-shaving ratio for data center load. Monte Carlo repeated experiments indicate that the improved Particle Swarm Optimization (PSO) reaches a 95% feasible solution rate with an average computation time of 26.8 s for day-ahead dispatch, which satisfies practical engineering requirements. Full article

This paper addresses the snaking instability of distributed electric-drive articulated vehicles under different speed conditions. A 7-DOF nonlinear vehicle model is established, and separate ideal reference models are constructed for the front and rear bodies. The particle swarm optimization is employed for offline optimization of the LQR weighting matrices, and a fuzzy adaptive PSO-LQR lateral stability control strategy is proposed. In this strategy, a fuzzy scheduling mechanism using vehicle speed and yaw rate as inputs is designed to adjust the LQR gains. Furthermore, a series of comparative simulations are conducted in this paper, and the simulation results are obtained under a fixed-speed sweep. Compared with the three-segment fixed-parameter LQR, the proposed method maintains lower articulated angles. At 11 m/s, the peak articulated angle is reduced by approximately 20%. On roads with adhesion coefficients of 0.2 and 0.8, fixed-parameter control suffers severe performance degradation. The proposed strategy maintains suppression performance on both friction surfaces. Comparative analyses with PSO linear interpolation and speed-only fuzzy control confirm the necessity of incorporating yaw rate as a second input. Further comparison with interval type-2 fuzzy control shows that both methods deliver comparable steady-state performance. The proposed method reduces computation time by approximately 56.1%. Full article

Particle swarm optimization (PSO) is a widely used bio-inspired optimization method. However, global guidance can align particle trajectories, reduce population diversity, and lead to premature convergence. To address this issue, we propose GRACE-PSO, a particle swarm optimizer based on group rank assessment and cooperative evolution. The method introduces group-best learning as an intermediate layer between personal-best and global-best learning to improve the balance between exploration and exploitation. GRACE-PSO integrates three coupled mechanisms: (1) a group-learning update that provides the population with multiple group-specific search directions; (2) a rank-based group-utility assessment that evaluates relative search effectiveness through pairwise comparisons of personal-best fitness values; and (3) a utility-driven adaptive strategy that adjusts the strengths of group-best and global-best learning and selectively reinitializes a small number of underperforming particles in stagnant groups. Experiments on 29 CEC 2017 benchmark functions at 30 and 50 dimensions against seven representative PSO methods show that GRACE-PSO achieves average ranks of 1.2414 and 1.3793, respectively. Experiments on two practical flexible intelligent metasurface optimization problems further demonstrate its competitive performance and practical applicability. Full article