Search Results (164)

In transient stability preventive control of power systems, time-domain simulation is computationally intensive. In addition, the initial operating feature data often contain abundant redundant and irrelevant information. These factors may adversely affect the assessment performance of machine learning models. To address these issues, a transient stability preventive control method based on the sample convolution and interaction network (SCINet) is proposed. First, a feature selection algorithm based on the orthogonal maximal information coefficient and information gain (OMICIG) is developed to extract the key operating features of the system. Second, the SCINet model is employed to learn the nonlinear mapping relationship between the selected key operating features and the transient stability index (TSI). Then, the trained SCINet model is embedded into the transient stability constrained optimal power flow (TSCOPF) model as a surrogate transient stability constraint. In this way, the complicated computation associated with nonlinear differential-algebraic equations (DAE) in the conventional TSCOPF model is avoided. Furthermore, an improved dung beetle optimizer (IDBO) algorithm is used to iteratively solve the resulting model, thereby deriving a preventive control strategy that ensures transient stability while maintaining system operating economy. Finally, simulation studies on the New England 10-machine 39-bus and the IEEE 118-bus system demonstrate the effectiveness of the proposed method. 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

Collaborative 3D path planning for multiple unmanned aerial vehicles (UAVs) in dense urban airspace is difficult, which does not come from one factor alone. Buildings, flight restrictions, moving obstacles, and inter-UAV coupling all act together, and the search space grows quickly as the scene becomes more crowded. In such cases, a standard swarm optimizer may still find a path, but it often struggles with early feasibility, later-stage refinement, and local replanning after the environment changes. To deal with these issues, this paper develops a spatio-temporal collaborative improved multi-strategy dung beetle optimization algorithm, called STC-IMSDBO, for urban multi-UAV path planning. The framework combines five linked components: feasible-airspace population initialization, spatio-temporal variable-step search, multi-factor adaptive weighting, local game-based conflict handling, and rolling-horizon replanning. A normalized multi-objective cost is used to balance flight efficiency, smoothness, obstacle avoidance, airspace compliance, and cooperative safety. The method is tested in four simulated urban scenarios and compared with six representative methods. In the tested cases, the STC-IMSDBO generates shorter feasible routes, uses less energy, converges in fewer iterations, and maintains better cooperative safety than the comparison methods. These results suggest that the method is a useful planning option for dense urban missions such as logistics, inspection, and emergency response. That said, larger-swarm runtime tests and field validation are still needed. Full article

The rapid advancement of unmanned aerial vehicles (UAVs) has greatly increased the application of various swarm intelligence algorithms in UAV path planning. To address the potential issues with the dung beetle optimizer (DBO) in UAV trajectory planning, such as low convergence accuracy, tendency to get trapped in local optima, and imbalance between global search and local exploration, a hybrid algorithm termed DBO-PSO is proposed by integrating DBO with particle swarm optimization (PSO) to solve the UAV path planning model. The Kent chaotic map is introduced to enhance population diversity and distribution uniformity, and the velocity–position update mechanism of PSO is incorporated into DBO to strengthen its global search capability. Comparative experiments are conducted on CEC2022 benchmark functions, and multiple classical swarm intelligence algorithms are selected for comparison using six evaluation metrics, along with Wilcoxon rank-sum and Friedman statistical tests. An ablation study is also performed to evaluate the contribution of each improvement component. The path planning experimental results demonstrate that compared to DBO, PSO, IDBO, and ECFDBO under the population size of 50, DBO-PSO reduces the total path cost by 44.2%, 17.3%, 8.9%, and 45.1%, respectively. The ablation study verifies that both improvement components contribute positively, which demonstrates its competitive performance and practical applicability in UAV three-dimensional path planning. The source codes to support the presented results are publicly available on GitHub. Full article

This study addresses the critical issues of rigid energy use and insufficient demand-side responsiveness in office buildings’ Variable Refrigerant Flow (VRF) systems under complex summer conditions. Existing research lacks fine-grained characterisation of short-term load fluctuations and often fails to accurately couple energy efficiency with humidity-adapted thermal comfort. To fill this gap, this paper proposes an integrated Model Predictive Control (MPC) framework driven by load characteristic identification and a novel hybrid prediction model. First, based on actual hourly metered data (683,280 records), K-means clustering was employed to identify three typical load patterns, pinpointing short-term peak loads in core office zones as the primary target for flexible regulation. Second, a high-precision GS-DBO-ELM prediction model—integrating Grid Search and Dung Beetle Optimisation—was developed to capture the nonlinear dynamics of VRF energy consumption and Predicted Mean Vote (PMV). The model achieved an R² of 0.99 with relative errors constrained within ±5%. Finally, a multi-objective MPC strategy, solved via an improved Artificial Hummingbird Algorithm (HAGSAHA) and weighted by the Analytic Hierarchy Process (AHP), was implemented to dynamically adjust zone-level temperature setpoints. Results demonstrate that the proposed MPC strategy reduces daily cooling energy consumption by 7.95–10.69% and peak loads by 15.3%, while maintaining strict thermal comfort (PMV within ±0.5). Under a time-of-use pricing mechanism, the flexible scheduling strategy achieved a 12.37% total electricity reduction and a 9.54% reduction in operating costs. This work provides a highly replicable, climate-tailored solution for low-carbon, flexible energy management in public buildings. Full article

This study addresses the high-precision position control problem of pilot-operated proportional directional valves under dead-zone nonlinearity. A fuzzy PID-based position control strategy optimized by the dung beetle optimizer (DBO-FPID) is proposed to alleviate switching lag and accuracy degradation caused by dead-zone effects. First, a refined nonlinear model combining theoretical analysis and AMESim simulation is established to quantitatively characterize the dead-zone evolution mechanism of the valve system, and the dead-zone range of the directional valve is identified as ±34.5% of the duty cycle. On this basis, a multiphysics co-simulation model is developed to analyze the static and dynamic characteristics of the pilot valve and the main spool. Then, the DBO algorithm is introduced to optimize the key parameters of the fuzzy PID controller by minimizing an objective function based on the integral of time-weighted absolute error (ITAE), thereby improving the controller’s compensation capability for dead-zone nonlinearity. Simulation results show that, compared with DBO-PID, the proposed DBO-FPID control strategy reduces the rise time by 54.4%. During triangular and sinusoidal position tracking, the dead-zone residence time is reduced by 47.5% and 44.8%, respectively, while the mean absolute error remains below 0.2 mm. Experiments further validate the effectiveness of the proposed control strategy for high-precision position control of the pilot-operated proportional directional valve. Full article

As engineering systems grow in complexity, reliable metaheuristic optimizers are increasingly essential. While swarm intelligence algorithms are widely applied, recent approaches like the Cuckoo Catfish Optimizer (CCO) can experience premature convergence due to limited local exploitation and simplistic boundary handling. To address these limitations, this paper proposes the Dung Beetle with Reflection CCO (DBRCCO), integrating two principal mechanisms. First, an adaptive local search strategy inspired by dung beetle foraging is incorporated to intensify exploitation within dynamically contracting regions. Second, a momentum-preserving reflecting boundary mechanism replaces traditional clamping, maintaining population diversity near constraint edges. DBRCCO is evaluated against eight contemporary metaheuristic algorithms using the 29 CEC2017 benchmark functions and a reservoir production optimization problem. Statistical analyses indicate that DBRCCO achieves competitive performance, securing a Friedman ranking of 1.5172 ($p<0.05$). In the reservoir application, DBRCCO improves the mean Net Present Value (NPV) by 12.54% while reducing variance by over 72% relative to the standard CCO. These findings suggest that DBRCCO offers a stable and effective alternative for complex optimization tasks. Full article

Mine ventilation network (MVN) regulation faces severe challenges: strong variable coupling, high search dimensionality, and the inherent conflict between energy conservation and safety constraints. To address these issues, we propose a novel airflow optimization framework integrating a Minimum Influence Spanning Tree (MIST), sensitivity attenuation boundaries, and an Improved Dung Beetle Optimizer (IDBO). Initially, high-influence co-tree chords are strategically extracted via MIST to compress the mathematical optimization dimensionality. Subsequently, effective ventilation resistance search intervals are bounded using sensitivity attenuation, preventing the algorithm from performing invalid searches in high-resistance regions. Furthermore, the standard DBO is enhanced via Fuchs chaotic initialization, Golden Sine and Lens Imaging collaborative learning, and differential mutation to minimize system power consumption. A 46-branch MVN case study validates the approach, identifying an 8-dimensional control combination as the absolute minimum requirement for full compliance. Compared to state-of-the-art baselines (DBO, SSA, WOA, DE), IDBO achieved the lowest power consumption. Post-optimization, the airflow constraint satisfaction rate improved from 89.13% to 100%, and total system power decreased by 11.87% (from 185.03 kW to 163.08 kW). Ultimately, this method robustly achieves Ventilation on Demand (VoD), providing a reliable computational tool for intelligent underground mining. Full article

In response to the current severe traffic congestion issues, highly reliable traffic flow prediction serves as a fundamental prerequisite for optimizing municipal road networks and mitigating systemic vehicular congestion. Aiming to elevate the precision of short-term traffic flow prediction, this paper first addresses the low precision of the Dung Beetle Optimizer (DBO) algorithm by introducing an exponential adaptive weight in the way of position update for the ball-rolling dung beetle, along with incorporating a Cauchy–Gaussian mutation strategy. We propose the Multi-strategy improved Dung Beetle Optimizer (MDBO), which is validated using eight benchmark test functions, demonstrating that MDBO outperforms common optimization algorithms in solution accuracy. Secondly, we adopt a combined prediction model, Traffic Flow Temporal-Spatio Network (TFTSNet), which constructs spatial feature modules and temporal feature modules in parallel fusion. Finally, we achieve short-term traffic flow prediction by optimizing the TFTSNet combined prediction model using MDBO. The experiment evaluated model performance using publicly available traffic flow datasets. The results demonstrate that, compared to other state-of-the-art models, the proposed joint prediction model based on MDBO-optimized TFTSNet achieves substantial enhancements in both prediction precision and generalization capability. Root mean square error (RMSE) decreased by 8.7–35.7%, mean absolute error (MAE) decreased by 6.6–40.0%, and R² reached 0.975, showcasing robust predictive capabilities and engineering reference value. Full article

Due to the non-stationary and nonlinear characteristics of diesel engine vibration signals, fault features cannot be fully extracted, which limits fault diagnosis performance. To address this issue, an improved fault diagnosis method combining optimized Variational Mode Decomposition with a Transformer and Support Vector Machine is proposed. An improved dung beetle optimization algorithm is employed to obtain optimal parameters for Variational Mode Decomposition. The envelope entropy minimization principle is applied to select the optimal intrinsic mode functions after Variational Mode Decomposition, achieving signal denoising. Analysis of variance is integrated for feature significance testing to screen critical features. The selected features are fed into a Transformer network for training. At the final classification stage, the traditional SoftMax classifier is replaced with a Support Vector Machine classifier. Full article

Under the combined pressures of dual carbon policy constraints, the integration of intelligent construction technologies, and intensifying market competition, the development of a scientific and robust performance evaluation system has become essential for building construction enterprises seeking to enhance their core competitiveness. Traditional evaluation methods, however, often suffer from incomplete indicator systems and limited capability in addressing high-dimensional and nonlinear problems, rendering them inadequate for the evolving demands of the industry. To address these challenges, this study proposes a performance evaluation model for building construction enterprises based on the least squares support vector machine (LSSVM), optimized by an improved Pied Kingfisher Optimizer (IPKO). Drawing on environment–behavior theory, the model incorporates three environmental and ten behavioral factors. To overcome the limitations of the original PKO algorithm—namely, insufficient exploration capability and weak local search—the exploration phase of PKO is integrated with that of the Marine Predators Algorithm. Empirical results demonstrate that: (1) the proposed IPKO outperforms Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Sparrow Search Algorithm (SSA), Dung Beetle Optimizer (DBO), Ospery Optimization Algorithm (OOA), and the original PKO in most benchmark functions; (2) the ReliefF feature selection algorithm improves the model’s test set accuracy by approximately 2.18%; and (3) the IPKO-LSSVM model achieves 6.53%, 4.16%, and 6.74% higher prediction accuracy than Backpropagation Neural Networks (BPNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), respectively. These findings highlight the model’s effectiveness in addressing small-sample, high-dimensional, and nonlinear problems, offering a scientifically sound and practical tool for performance evaluation in building construction enterprises. Full article

This study investigates mechanism-based modeling and simulation of a single-shaft heavy-duty industrial gas turbine. Taking the PG9171E gas turbine as the case study, component-level steady-state and dynamic models are developed. The steady-state model is established using the constant mass flow (CMF) method. For dynamic modeling, both the CMF approach and the inter-component volume (ICV) approach are implemented to enable a comparative assessment of the two methods. On the basis of the steady-state model, an improved Dung Beetle Optimization (DBO) algorithm is proposed to perform model correction using measured operational data from the gas turbine. After model correction, the maximum relative error between the simulated results and the measured operating data is reduced to 1.01 × 10⁻⁵%. Following high-accuracy model correction, sensitivity analysis and a comparative dynamic study are conducted for the two dynamic modeling approaches. The results indicate that the most influential sensitivity parameter is the rotor rotational inertia, followed by the virtual volume of the combustor. Moreover, the primary discrepancy between the ICV and CMF approaches arises from differences in the operating trajectories on component characteristic maps. The ICV-based model exhibits a pronounced response lag; however, it requires less computational time than the CMF-based model, making it more suitable for rapid engineering simulation and practical applications. Full article

Cluster Head (CH) selection is a crucial process in clustered Wireless Sensor Networks (WSNs) because it directly affects energy balance and network lifetime. However, CH selection is an NP-hard optimization problem, and many metaheuristic-based methods suffer from limited search diversity and premature convergence, leading to uneven energy dissipation. This paper formulates CH selection as a multi-criteria energy-aware optimization problem and proposes an Enhanced Secretary Bird Optimization Algorithm (ESBOA). The proposed ESBOA improves the original Secretary Bird Optimization Algorithm by integrating logistic chaotic map-based population initialization to enhance early-stage exploration and an iterative local search mechanism to strengthen solution refinement in later iterations. A multi-criteria fitness function considering residual energy, distance to the base station, and node degree explicitly guides the optimization toward energy-efficient clustering. The proposed method is implemented in a Python 3.11.9-based simulation framework using a first-order radio energy model and evaluated against standard SBOA, Crested Porcupine Optimization (CPO), and Dung Beetle Optimization (DBO). Simulation results demonstrate that ESBOA preserves more alive nodes, maintains higher residual energy, delivers more cumulative packets to the base station, and extends network lifetime, achieving approximately 3–13% improvement in last node death (LND) compared with the standard SBOA. Full article

High-voltage circuit breakers (HVCBs) are critical switching devices whose mechanical reliability directly affects power system safety and operational continuity. Accurate fault diagnosis remains challenging due to nonlinear vibration characteristics and the sensitivity of support vector machines (SVMs) to hyperparameter selection. To address this issue, a multi-strategy improved dung beetle optimization–support vector machine (MIDBO–SVM) framework is proposed for vibration-based mechanical fault diagnosis. Frequency-domain features are extracted from vibration signals using the fast Fourier transform to characterize fault-related spectral variations. A multi-strategy improved dung beetle optimization (MIDBO) algorithm incorporating chaotic initialization, adaptive search regulation, and mutation enhancement is developed to improve population diversity, global exploration, and convergence stability. The optimized MIDBO is used to determine the penalty and kernel parameters of the SVM, constructing a robust and well-generalized diagnostic model. Experimental results show that MIDBO–SVM achieves a diagnostic accuracy of 96.67%, outperforming conventional SVM (86.25%) and random forest (89.17%). The proposed method also demonstrates faster convergence and maintains accuracy above 86% under imbalanced sample conditions, confirming its robustness and generalization capability. These advantages contribute to more reliable mechanical condition assessment and improved maintenance decision support for HVCBs. Full article

Achieving sustainability in prefabricated construction necessitates a balance between operational efficiency and stringent environmental constraints. However, cascading rework chains triggered by assembly defects frequently disrupt this equilibrium. Existing literature predominantly addresses this dynamic through reactive rescheduling, thereby largely overlooking the potential of proactive topological interception. To bridge this gap, this study proposes a proactive bi-level scheduling framework that mathematically integrates strategic quality inspection planning with operational low-carbon project execution. Specifically, a Generalized Total Cost (GTC) model is formulated to internalize multi-objective trade-offs—including time, cost, and carbon emissions—into a unified financial metric through market-based shadow prices. This framework is operationalized through a novel bi-level Hybrid Evolutionary Algorithm (H-TS-CDBO). By combining the global exploration capabilities of Chaotic Dung Beetle Optimization with the local refinement mechanisms of Tabu Search, the proposed solver is specifically engineered to navigate the topological ruggedness induced by proactive inspection interventions. Empirical benchmarking validates the computational robustness of the solver, while an illustrative case study substantiates a critical managerial paradigm shift from “passive remediation” to “active prevention”: compared to traditional methods, a marginal preventive investment of 5.4% functions as an effective containment mechanism, yielding a 40.8% net reduction in the GTC. Furthermore, a sensitivity analysis regarding varying static carbon tax rates simulates algorithmic adaptation under diverse regulatory intensity thresholds, delineating an actionable pathway for project managers to achieve lean, low-carbon synergy amidst evolving regulatory pressures. Full article