Search Results (8,152)

The rapid proliferation of cloud data centers has intensified concerns over carbon emissions, energy efficiency, and sustainability. Virtual machine (VM) placement is a pivotal control lever, yet existing methods rarely couple carbon intensity signals with computationally tractable multi-objective optimization. In this paper, we propose CASO (Carbon-Aware Surrogate-Guided Optimization), a novel framework that integrates an online adaptive Radial Basis Function (RBF) surrogate model with a self-adaptive hybrid PSO-DE swarm optimizer for real-time VM placement in geo-distributed edge cloud environments. CASO simultaneously minimizes carbon emissions, energy consumption, SLA violation rate, and network latency under strict host capacity and Quality-of-Service (QoS) constraints. Three key innovations differentiate CASO: (i) an online surrogate update mechanism that refines fitness approximations incrementally as workload patterns evolve; (ii) a carbon intensity weighting scheme anchored to real-time Grid Emission Factor (GEF) signals; and (iii) an adaptive parameter controller that autonomously tunes swarm exploration–exploitation trade-offs without hand-crafting. Experiments on the publicly available Alibaba Cluster Trace (cluster-trace-v2026-GenAI) dataset within a CloudSim-Plus environment show that CASO reduces carbon emissions by up to 31.4%, energy consumption by 27.9%, and SLA violations by 18.8% compared to the strongest baseline while converging 3.8× faster than the strongest baseline (ADEDL). Full article

Urban Unmanned Aerial Vehicle (UAV) logistics operations are frequently constrained by the intersection of limited battery endurance and dynamic ground traffic. When mobile depots are delayed by congestion, onboard drone fleets experience extended idling periods, leading to constrained sortie generation and reduced asset utilization. To address this bottleneck, this paper introduces a traffic-predictive multi-UAV dispatch framework for deterministic day-ahead planning under modeled urban operating conditions. By coupling a count-derived macroscopic speed surrogate learned using XGBoost with a Particle Swarm Optimization (PSO)–Mixed-Integer Linear Programming (MILP) optimization architecture, the framework synchronizes mobile depot trajectories with forecasted low-congestion windows and pre-allocates endurance-feasible parallel aerial sorties. Controlled computational experiments across 30 synthetic routing instances demonstrate the potential value of this approach within the stated modeling assumptions. Compared to baseline clustered deployments, the traffic-aware framework raises mean fleet utilization from 0.43 to 0.63—a 46.2% relative improvement driven by temporal compression of the mission window rather than an absolute increase in flight hours. Furthermore, the proposed framework reduces total mission completion time by 69.87% relative to the conventional truck-only baseline, while achieving a 29.58% incremental gain over static speed drone deployments. These findings suggest that incorporating predictive ground traffic information into day-ahead UAV scheduling can improve modeled fleet efficiency; however, field validation with measured route-level speeds, real delivery demand, and operational constraints remains necessary before deployment-level claims can be made. Full article

Complex networks have become a fundamental paradigm for modeling real-world systems. Synchronization of such networks, particularly under hyperchaotic dynamics, presents a significant control challenge due to the high-dimensional state space and multiple positive Lyapunov exponents. This paper addresses the driver node selection problem in a 4D Hyperchaotic Lorenz complex network, formulating it as a constrained binary optimization task. We evaluate a pool of advanced metaheuristics, including the quantum genetic algorithm (QGA), seahorse optimizer (SHO), and artificial bee colony (ABC), across multiple network experiments conducted over 30 independent runs to guarantee statistical validity. The performance of these solvers is rigorously benchmarked against traditional topological heuristics, a random selection baseline comprising 600 feasible configurations, and verified through Wilcoxon statistical testing. Furthermore, addressing computational sustainability, we introduce a “Green-Artificial Intelligence” architecture based on dual-tier structured query language memoization (SQL-memoization) and provide a detailed runtime comparison evaluating its efficiency. The empirical results indicate that swarm-intelligence methods such as ABC and SHO exhibit robust competitive performance in minimizing synchronization errors while the Green-AI framework consistently and drastically reduces the computation of the repetitive simulations. Full article

Roof stability plays a crucial role in maintaining the overall stability of surrounding rocks to ensure safety of tunnel construction and operation. In this work, tension cut-off (TC) technique is introduced to modify the Hoek–Brown (HB) criterion to describe the tensile failure of rock strata. Thereafter, stability analysis of anchor bolt-reinforced tunnel roofs in rock strata subjected to a hybrid tensile-shear fracture is performed. The work balance equation is established by equating the external work rates of the falling block and the anchor bolts to the internal energy dissipation rate. Two stability indicators, that is the stability number (N) and the factor of safety (FoS) are proposed to quantitatively analyze the stability of tunnel roofs. Optimization algorithms combining genetic algorithm and particle swarm optimization are programmed to capture the optimal upper bound solutions. The influences of TC, strength criterion parameters, and anchor bolt-reinforcement strength on roof stability are explored in this work. It was found that increasing the anchor tension T improves the FoS of reinforced tunnel roofs, with an increase of up to 68% observed for rectangular tunnel roofs under the selected representative case, while the improvement is relatively less pronounced for circular tunnel roofs. Regarding anchor support, as ξ increases, the N for rectangular tunnels nearly doubles. This work provides a theoretical basis for preliminary designing of tunnels in reinforced rock strata. Full article

Accurate detection of internal defects in rolls is crucial for industrial safety and product quality. Ultrasonic testing is a mainstream non-destructive method widely used for this purpose. However, in practice, ultrasonic echo signals often suffer from background clutter. When defects are located near the surface, weak defect echoes tend to couple with surface echoes, making signal extraction difficult and reducing the accuracy of subsequent feature extraction and classification. This paper proposes a novel ultrasonic signal preprocessing method aimed at improving the performance of subsequent defect identification models. The method first acquires ultrasonic signals from defect regions and background clutter reference signals from defect-free regions using a digital ultrasonic flaw detector. An improved median filter is then applied to remove spike interference and boundary outliers. On this basis, a multi-stage FIR (finite impulse response) filter is constructed, and particle swarm optimization is employed to adaptively optimize filter parameters, achieving an accurate estimation of background clutter. Finally, the clutter-suppressed defect signal is obtained through signal subtraction. Experimental results on a dataset of 5000 samples (2500 defective, 2500 non-defective) containing cylindrical artificial defects (diameter 8 mm, length 30 mm) demonstrate that using a CNN classifier with the same feature extraction and classification model, the signals preprocessed by the proposed method outperform traditional median filtering and wavelet denoising methods. The defect identification accuracy is improved by approximately 38 percentage points compared to median filtering and 20 percentage points compared to wavelet denoising, while also achieving a high recall rate, validating the effectiveness of the proposed method in enhancing roll internal defect detection. Full article

To address the problems of false negatives, false positives, and delayed response in traditional fire detection systems, this paper proposes a warehouse fire detection scheme based on multi-sensor information fusion. By constructing a ZigBee wireless sensor network and integrating temperature, CO concentration and smoke sensors, fire simulation data are collected in the warehouse. At the data processing level, an improved Grubbs criterion is innovatively adopted to eliminate outliers, and the median is used instead of the average to effectively suppress the same-side shielding effect. At the feature layer fusion stage, a BP neural network model optimized by the cosine decreasing inertia weight particle swarm optimization algorithm (CIW-PSO) is designed. By dynamically adjusting the learning factors (c₁, c₂) and inertia weight (w), the convergence speed and global optimization ability are significantly improved. At the decision-making level, a fuzzy logic reasoning mechanism is introduced to integrate multi-parameter membership functions, thereby reducing the probability of misjudgment. Field tests have verified that the system can achieve early fire warning in a 50 m × 100 m warehouse environment, with a false alarm rate reduced by 42% compared to a single sensor and a response time shortened by 35%, providing an efficient and reliable intelligent solution for warehouse fire safety. Full article

We analyze the standard multi-particle particle swarm optimization (PSO) algorithm with global-best (all-to-all) topology and constant hyperparameters on smooth strongly convex objectives. By rewriting the PSO velocity recursion as a stochastic heavy-ball method acting on a time-varying quadratic surrogate defined by the personal and global bests, and by applying a Lyapunov drift argument in the style of stochastic momentum analyses, we obtain mean-square convergence of particle positions to the unique minimizer and convergence of the best-so-far objective gaps. The deterministic PSO obtained by fixing the random coefficients at their mean values appears as a noise-free special case of the same Lyapunov framework. Full article

Regarding the path planning problem for the local leveling operation of the land leveler, this paper proposes a path planning method based on the improved dung beetle optimizer (IDBO) algorithm. Firstly, a comprehensive evaluation objective function was established for the local operation path planning of the land leveler, which included the path length, under-excavation amount, under-filling amount, as well as the total amount of excavated and filled soil. Then, IDBO algorithm was constructed, an initialization population strategy based on Fuch chaotic mapping and reverse learning strategy was designed, as well as an improved ball-rolling behavior that integrates the search strategy of the Aquila high soar with the vertical stoop from the Aquila optimizer algorithm. Test functions were used to verify the superiority of the IDBO algorithm compared to the dung beetle optimizer (DBO) algorithm, the particle swarm optimization (PSO) algorithm and the gray wolf optimizer (GWO) algorithm. Finally, taking the paddy fields in a real environment as the object, a hardware platform for data acquisition was constructed, and data collection, analysis, terrain modeling, and path planning experiments were carried out with paddy fields in the natural environment as the measured objects. The experimental results show that, for the primary optimization objective of load variation cost, as well as path length cost, compared with the other three algorithms(PSO, GWO, DBO), the IDBO algorithm achieved improvements of 7.0%, 12.3%, and 6.6% on Plot 1, 1.6%, 9.2%, and 1.6% on Plot 2, 4.0%, 6.1%, and 1.5% on Plot 3, and 3.3%, 24.1%, and 3.4% on Plot 4. Full article

For microbiological diagnostics, accurately counting and segmenting microbial colonies is extremely important. However, manual methods are labor-intensive and yield inconsistent results. We develop a hybrid model using swarm intelligence, combining a convolutional transformer with nested skip connections and global context with channel and spatial attention. Parameter tuning is supported by a variety of swarm optimization algorithms (e.g., Particle Swarm Optimization, Quantum-behaved Particle Swarm Optimization, and Differential Evolution Particle Swarm Optimization). Morphological refinement, including a further watershed transform, an attention graph, and post-processing, enhances colony boundaries by separating them. Grad-CAM++, Integrated Gradients, and temperature scaling provide a transparent and trustworthy model through explainability and post hoc calibration. The proposed model was extensively tested on the Microbial Colony Recognition and Circular Bacterial Colony Datasets, achieving a Dice score of 94.2%, an Intersection over the Union of 88.6%, and a mean absolute counting error of 2.7 colonies. These results significantly outperform several baseline models, including U-Net (88.1%), U-Net++ (89.7%), Attention U-Net (90.6%), and Swin-Unet (91.4%). Statistically significant improvements were confirmed (p < 0.01). A cross-dataset analysis demonstrates the framework’s robustness and cross-domain applicability, and positions it as a trustworthy, explainable automated model for assessing microbial colonies in laboratory and clinical settings. Full article

When operating in complex and obstacle-dense environments, micro UAV swarms often face severe cooperative positioning failures due to transient non-line-of-sight (NLOS) interference and cascaded inertial sensor drift. To address this, this work proposes a fault-tolerant positioning framework integrating multi-agent deep reinforcement learning with cooperative extended Kalman filtering (MADRL-CEKF). The system incorporates a link-level dynamic soft isolation mechanism that dynamically adjusts observation covariance to effectively sever paths of cooperative error contagion. An adaptive Markov smoothing constraint is mathematically embedded to mitigate high-frequency control jitter typical of AI-driven policies. Crucially, the framework implements a resource-aware multi-objective reward architecture tailored for micro UAVs. Evaluated through high-fidelity simulations and offline physical datasets, the proposed framework achieves a 96.01% reduction in average tracking error (RMSE) under extreme multi-node cascaded failures, completely preventing system divergence. Furthermore, through autonomous multi-objective trade-offs, the system reduces processing delay by 44% (to 25.1 ms) and computational energy consumption by 41% with only a marginal accuracy compromise of 0.16 m, strictly keeping the execution time within the 50 ms real-time threshold. The MADRL-CEKF framework effectively bridges the gap between sophisticated AI decision-making and strict engineering constraints, providing a highly robust and resource-efficient navigation paradigm for swarm robotics. Full article

This study proposes an experimental data-driven hybrid prediction framework for hydraulic turbine performance using a Particle Swarm Optimization-enhanced Extreme Learning Machine (PSO-ELM). The performance of three hydraulic turbines, namely Pelton, Kaplan, and Francis turbines, was experimentally investigated under different jet-opening and guide-vane conditions. The experimental results showed that the Pelton turbine (PT) achieved its highest efficiency at low jet opening, whereas the Kaplan and Francis turbines performed better at higher guide-vane openings. The measured data includes 36 tests, which were then used to evolve and evaluate hybrid ML models for predicting hydraulic power and efficiency. Jet-opening or guide-vane position (25%, 50%, 75% and 100%) and rotational speed were used as input variables, while brake power and efficiency were used as output variables. The proposed PSO-ELM model was compared with other optimized ELM models, including Genetic Algorithms Extreme Learning Machine (GA-ELM), Differential Evolution Extreme Learning Machine (DE-ELM), and Whale Optimization Algorithm Extreme Learning Machine (WOA-ELM), as well as Particle Swarm Optimization–Adaptive Neuro-Fuzzy Inference System (PSO-ANFIS) and Particle Swarm Optimization–Multi-Layer Perceptron (PSO-MLP) models. The suggested method presents a hopeful structure for tackling the difficulties linked to performance evaluation, thus enabling a more dependable and effective use of energy resources. The main findings validate that a PSO-based structure reaching an R² value of 0.997 is more efficient in predictive tool performance optimization for hydropower systems. Full article

The existing arresting efficiency evaluation method overlooks corrosion defects in its formulation. If directly applied to evaluate and design arrestors for corroded sandwich pipes, it often leads to conservative evaluations of arresting efficiency and unreasonably designed arrestors. Based on this, this paper first verifies the reliability of numerical simulation results through physical experiments. On this basis, the influence of the structural parameters and material parameters of the arrestor on the arresting efficiency of the integral arrestor is analyzed. The results show that an increase in the length, thickness and material strength of the arrestor not only affects the arresting efficiency of the arrestor but also changes the arresting crossing mode, from parallel crossing to orthogonal crossing. A chart of arresting efficiency suitable for engineering design is proposed. Finally, a systematic comparison is conducted of different modeling methods. The results show that, considering both prediction accuracy and training efficiency, the Genetic Algorithm–Back Propagation (GA-BP) model significantly outperforms the empirical model, the Whale Optimization Algorithm–Back Propagation (WOA-BP) model, and the Particle Swarm Optimization–Back Propagation (PSO-BP) model. The average prediction error is only 6.56%, and 94.42% of the data error is less than 20%. The model provides a theoretical basis for the arrestor design and failure assessment of sandwich pipes with corrosion defects and has clear engineering guidance value. Full article

The construction cost of an offshore wind farm collection system accounts for 15–30% of the total investment, and its efficient design is crucial to the economy; however, traditional methods in large-scale scenarios suffer from slow convergence and local optimization problems. In this study, we propose an upper and lower topology optimization framework based on the sled dog optimizer (SDO). The upper layer adopts polar coordinate partitioning combined with dynamic minimum spanning tree (DMST) to realize wind farm partitioning, and deals with the current-carrying capacity constraints and cable no-crossing requirements in a synchronized manner. The lower layer applies the SDO algorithm to optimize the topology structure within the partitioning range. The performance of genetic algorithm (GA), immunity algorithm (IA), particle swarm optimization (PSO), and SDO approaches is compared by the dynamic minimum spanning tree method through the case of an offshore wind farm with 62 wind turbines (WTs). The results show that the SDO-DMST framework significantly outperforms the comparison algorithms in terms of computational efficiency and cost optimization, and the proposed method can stably obtain high-quality cable topology solutions, which proves its superiority in unit group partitioning and cable routing co-optimization. In this paper, the SDO is introduced to collection system optimization for the first time, providing an efficient and robust design solution for large-scale offshore wind farms. Full article

Designing lightweight composite sandwich structures is challenging due to the conflicting objectives of minimizing structural weight and cost while satisfying strength and stiffness requirements. The optimization procedure becomes more complex when multiple discrete design variables and nonlinear material behavior are involved. This study presents a newly developed optimization methodology for a sandwich structure composed of Fiber Reinforced Polymer (FRP) laminated facesheets and an aluminum honeycomb core. To reduce the computational cost associated with repeated high-fidelity Finite Element (FE) analyses, a surrogate modeling strategy based on Artificial Neural Networks (ANNs) is employed to approximate the structural response. The applied dataset is generated using Monte Carlo simulation in which combinations of design variables are used as inputs, and the corresponding structural responses obtained from the analytical formulation are used as outputs for training the ANN surrogate model. The trained ANN model is integrated with a Multi-Objective Niching Memetic Particle Swarm Optimization (MO-NMPSO) algorithm to simultaneously minimize structural weight and material cost while satisfying constraints on facesheet strength, wrinkling, intra-cell buckling, deflection, core shear failure and structural thickness. The resulting Pareto-optimal solutions are validated through detailed FE simulations, demonstrating the reliability of the newly elaborated optimization framework. The results of the newly developed computationally efficient optimization procedure provide a diverse set of optimal design solutions for the investigated sandwich structure. Full article

To address the issues of strong inter-well interference during multi-well fracturing in shale reservoirs, low efficiency of conventional numerical simulation, and the tendency of machine learning models to overfit and lack interpretability under small-sample conditions, this paper constructs an explainable ensemble learning framework for predicting hydraulic fracture asymmetry. A geology–engineering integrated numerical simulation is adopted to quantify the fracture asymmetry index η as an interference metric, and an initial dataset is constructed comprising natural fracture orientation, well spacing, and injection rate. Subsequently, Jensen–Shannon (JS) divergence-constrained Gaussian data augmentation and second-order interaction features are introduced, and the GBRT model parameters are optimized using particle swarm optimization (PSO). Furthermore, random forest and ridge regression are incorporated, and ensemble weights are determined via cross-validation to build a weighted ensemble prediction model. The results show that the proposed model achieves good predictive performance in repeated validation, with an average coefficient of determination R² of 0.8484 and a 95% confidence interval of 0.8179–0.8790, while also demonstrating favorable overall accuracy in multiple baseline model comparisons and regularization-controlled experiments. Through leave-one-simulation-scenario validation, prediction interval analysis, and interpretability robustness testing, the model’s generalization boundary, prediction uncertainty, and explanation reliability under small-sample conditions are further evaluated. SHAP analysis and grouped permutation importance results indicate that the natural fracture angle is the dominant factor controlling asymmetric fracture response, while the interaction between well spacing and the natural fracture angle also significantly affects the predictions, suggesting that asymmetric fracture propagation is primarily governed by the combined effects of natural fracture steering and inter-well stress interference. The proposed framework can serve as a fast surrogate model for evaluating inter-well interference and screening fracturing designs within a given simulation parameter space, providing an interpretable data-driven approach for fracturing design optimization in shale reservoirs under small-sample conditions. Full article