Search Results (164)

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

Accurately identifying damage in beam structures remains a tough challenge, the global trend of wavelet coefficients easily swallows faint local defect signatures, and high-dimensional model updating is computationally inefficient. To tackle these problems, this paper introduces a robust two-stage framework for damage identification that combines a residual-based wavelet strategy with an Improved t-distribution Dung Beetle Optimizer (ITDBO). Rather than relying on guesswork for wavelet selection, we introduce the Residual-based Wavelet Contrast Index (RWCI). By actively stripping away the global trend embedded within the wavelet coefficients, RWCI isolates the pure residual data, drastically amplifying the contrast between genuine stiffness loss and ambient noise for precise damage localization. With the search zone narrowed down, we deploy the ITDBO to quantify the severity. Powered by Bernoulli chaotic mapping and a t-distribution perturbation mechanism, ITDBO effectively bypasses the curse of dimensionality and entirely avoids the premature convergence traps that plague standard metaheuristics. Validated through both numerical simulations and physical experiments on a one-dimensional fixed-fixed steel beam, this hybrid approach proves its mettle. The framework not only accurately flags the defects through heavy noise but also locks onto the exact damage severity with unprecedented efficiency and stability. Full article

Bactrian camels (Camelus bactrianus) are economically vital livestock in arid regions; however, their intestinal virome is poorly understood. We employed viral metagenomics to analyze intestinal tissue samples from deceased camels at a breeding facility in Urumqi, Xinjiang, China, and uncovered a diverse viral population dominated by dromedary camel bocavirus (DBoV1) and porcine astrovirus (PoAstV5). A molecular epidemiological survey of 261 anal swab samples collected across Xinjiang revealed prevalence rates of 36.40% (95/261) for DBoV1 and 26.44% (69/261) for PoAstV5, indicating their widespread circulation. Phylogenetic analyses of the DBoV1 NS1 and PoAstV5 ORF1a genes showed close relationships with known strains, with no evidence of recombination. This study expands the known viral spectrum of Bactrian camels, marking the first report of PoAstV5 in this species, a finding suggestive of cross-species transmission. These results enhance our understanding of camel viral diversity and provide critical data for managing enteric diseases in camel populations, with potential implications for livestock health and surveillance of zoonotic risks. Full article

The lightweight design of box-type double-girder overhead crane main girders is important for improving load-carrying capacity, reducing energy consumption, and enhancing transportation efficiency. However, the structural optimization of crane main girders involves multiple constraints and strong nonlinearity, which often leads to slow convergence and premature stagnation when using traditional optimization methods. To address these issues, a multi-strategy improved dung beetle optimization algorithm (MSIDBO) is proposed for the lightweight design of overhead crane main girders. First, the search mechanism and inherent limitations of the standard dung beetle optimization (DBO) algorithm are analyzed. Subsequently, several enhancement strategies are introduced, including hybrid chaotic population initialization; reflective boundary handling; adaptive quantum jump updating; adaptive hybrid updating; and a staged control strategy for search intensity. These strategies are designed to enhance population diversity and achieve a better balance between global exploration and local exploitation. The performance of MSIDBO was evaluated on 29 CEC2017 benchmark functions. The results show that MSIDBO generally converges faster on 25 functions and reaches the global optimum on 24 functions among the compared algorithms. Finally, based on mechanical analysis and design specifications of overhead crane main girders, a constrained structural optimization model is established. The lightweight design optimization is carried out, and finite element simulations were conducted using ANSYS Workbench to verify the effectiveness and engineering feasibility of the optimized design. The results show that the proposed MSIDBO algorithm exhibits enhanced stability and convergence performance, achieving a weight reduction of 19.4% in the main girder under the specified design configuration, meeting satisfying strength and safety requirements. Full article

Unmanned aerial vehicles (UAVs), a key enabler for the Internet of Things’ (IoT) evolution to 3D spatial dimensions, play a critical role in data collection across fields. However, path planning in obstacle-rich and threat-prone environments remains a core bottleneck for their safe and efficient operation. Traditional meta-heuristic algorithms suffer from insufficient exploration, slow convergence, and local optima issues. To address this, we propose an enhanced multi-mechanism DBO algorithm (MMDBO), integrating SPM chaotic mapping, dynamic global exploration, adaptive T-distribution, and dynamic weight mechanisms. Comparative experiments against five classical algorithms on 12 benchmarks test functions and three complex terrains show MMDBO achieves superior performance across the majority of key path-planning metrics—including flight trajectory length, altitude profile fidelity, and path smoothness—while incurring only a modest increase in computational time. The results of the statistical test further indicate that the MMDBO algorithm significantly outperforms the comparison algorithms in both convergence speed and accuracy. These advances deliver actionable, highly reliable guidance for UAV flight path optimization. Full article

Propelled by the “dual-carbon” strategy, green and intelligent ports are rapidly advancing toward low-carbon and intelligent development. However, the large-scale incorporation of renewable energy and the extensive electrification of transport equipment have substantially heightened system volatility and scheduling complexity. To address the challenges associated with multi-energy coupling and economic operation in medium and large ports, a hierarchical collaborative optimization scheduling strategy is proposed. The upper layer employs an improved Bio-enhanced Dung Beetle Optimization (BDBO) algorithm for parameter optimization and carbon-cost minimization. Meanwhile, the lower layer establishes a rolling time-series control mechanism grounded in Adaptive Dynamic Hierarchical Decoupling Planning (ADHDP), thereby constituting an integrated BDBO-ADHDP dual-agent system. Simulation results across four seasonal scenarios demonstrate that the proposed methodology outperforms DQN, PSO, GA, ACO, and DBO algorithms in reducing grid power purchases, enhancing renewable energy utilization, mitigating curtailment, and lowering operational costs. Moreover, it achieves faster convergence, superior robustness, and effective carbon-emission control. This study substantiates the efficacy of the proposed strategy within green port integrated energy systems and highlights its potential for broader application in other multi-energy coupled systems. Full article

In view of the highly coupled, time-varying, and susceptible to differences in aircraft configuration of the Boeing 787 Electric Environmental Control System (E-ECS), a simplified mechanism model based on effectiveness-number of transfer units is proposed. Firstly, considering the influence of differences in aircraft configuration, part number, and optional components, a heat conduction correction coefficient is introduced to adjust the calculation process of heat exchange efficiency. Secondly, the steady-state characteristic equation of the electric compressor/turbine is established by utilizing the principle of isentropic work. Then, the outlet temperature value of the water removal component is calculated by using secondary heat recovery technology. Finally, to solve the problem of easily getting stuck in local optima during high-dimensional parameter identification, an adaptive hybrid optimization algorithm combining Dung Beetle Optimization (DBO) with mutation operator and Particle Swarm Optimization (PSO) is proposed. The experimental results show that the proposed mechanism model can achieve dynamic representation of the outlet temperature of each component of E-ECS under different aircraft stages. The DBO-PSO algorithm has a fast convergence speed and a low probability of falling into local optima. The temperature values calculated by the model have high computational accuracy, which can provide reliable data support for component level E-ECS health monitoring and early fault warning. Full article

Early and accurate prediction of the remaining useful life (RUL), defined as the number of operational cycles a battery can continue to function before reaching its end-of-life threshold, is crucial for improving the reliability of new energy vehicles. To address noise contamination, capacity regeneration effects, and data scarcity in early-stage prognostics, this paper proposes a hybrid framework integrating signal decomposition, time series augmentation, and deep forecasting. The raw capacity sequence is decomposed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to separate multi-scale components. A Transformer-enhanced time series generative adversarial network (HyT-GAN) is then employed to augment decomposed components, improving robustness under small-sample conditions. A CNN-BiGRU predictor is trained for capacity forecasting, and key hyperparameters are tuned via the Dung Beetle Optimizer (DBO). Experiments on NASA and CALCE benchmark datasets demonstrate that the proposed method achieves accurate early-stage prediction using only 20% historical data, with $R^2$ ranging from 0.9643 to 0.9972 and RMSE/MAE below 0.0296/0.0198. These results indicate that the proposed framework can deliver reliable RUL estimates under data-limited and noisy measurement conditions. Full article

With the increasing application of unmanned aerial vehicle (UAV) in multiple fields, the path planning problem has become a key challenge in the optimization domain. This paper proposes an Improved Artificial Lemming Algorithm (IALA), which incorporates three strategies: the optimal information retention strategy based on individual historical memory, the hybrid search strategy based on differential evolution operators, and the local refined search strategy based on directed neighborhood perturbation. These strategies are designed to enhance the algorithm’s global exploration and local exploitation capabilities in tackling complex optimization problems. Subsequently, comparative experiments are conducted on the CEC2017 benchmark suite across three dimensions (30D, 50D, and 100D) against eight state-of-the-art algorithms proposed in recent years, including SBOA and DBO. The results demonstrate that IALA achieves superior performance across multiple metrics, ranking first in both the Wilcoxon rank-sum test and the Friedman ranking test. Analyses of convergence curves and data distributions further verify its excellent optimization performance and robustness. Finally, IALA and the comparative algorithms are applied to eight 3D UAV path planning scenarios and two amphibious UAV path planning models. In the independent repeated experiments across the eight scenarios, IALA attains the optimal performance 13 times in terms of the two metrics, Mean and Std. It also ranks first in the Monte Carlo experiments for the two amphibious UAV path planning models. Full article

In the complex urban path planning of unmanned ground vehicles (UGVs), the dung beetle optimization (DBO) algorithm is widely used due to its simple structure and fast convergence speed. However, it still has the disadvantages of poor convergence accuracy and is easy to fall into a local optimum. To solve these problems, this paper proposes a multi-strategy enhanced DBO algorithm (ESDBO). Firstly, sine mapping is introduced in the population initialization stage to enhance solution diversity. Secondly, an adaptive information volatilization mutation strategy is proposed, which dynamically balances the convergence and global search ability. Finally, a multi-mechanism co-evolution strategy is designed, which significantly improves the local search ability and stability. Through ablation experiments and CEC2017 benchmark tests, the optimization ability of the proposed strategy and the convergence accuracy and stability of ESDBO are verified. Further path planning experiments are carried out on the public Random MAPF benchmark map. The results show that ESDBO can generate global optimal paths with short path length, few turns, and high safety margin on different obstacle densities and map scales. The algorithm provides an efficient and reliable solution for autonomous navigation in complex urban environments. Full article

To enhance the accuracy of wind power forecasting, this paper proposes a hybrid model that integrates Variational Mode Decomposition (VMD), Improved Dung Beetle Optimization (IDBO) and Support Vector Machine (SVM). First, to reduce the volatility and non-stationarity of wind power data, VMD is applied to decompose the original signal into several intrinsic mode functions (IMFs). Subsequently, the Dung Beetle Optimization (DBO) algorithm is improved using chaotic mapping, a Lévy flight search strategy and adaptive t-distribution. Finally, the penalty coefficient of the SVM is optimized using IDBO, and the VMD-IDBO-SVM model is constructed. This study proposes an improved IDBO algorithm and, for the first time, integrates VMD and IDBO-SVM within the context of wind power forecasting. Experimental results show that the proposed VMD-IDBO-SVM model achieves a MAE of 3.315, an RMSE of 4.130, and an R² of 0.985 on test data from a wind farm, demonstrating a significant improvement compared with the traditional SVM model. It has demonstrated excellent stability and significance in both multi-time-slice validation and statistical testing. Full article

Accurately predict the external corrosion rate is crucial for the integrity management and risk assessment of buried pipelines. However, existing prediction models often suffer from limitations such as low accuracy, instability, and overfitting. To address these challenges, this study proposes a novel hybrid model, FA-IDBO-KELM. Firstly, Factor Analysis (FA) was employed to reduce the dimensionality of ten original corrosion-influencing factors, extracting seven principal components to mitigate multicollinearity. Subsequently, the hyperparameters (penalty coefficient C and kernel parameter γ) of the Kernel Extreme Learning Machine (KELM) were optimized using an Improved Dung Beetle Optimizer (IDBO). The IDBO included four key enhancements compared to the standard DBO: spatial pyramid mapping (SPM) for population initialization, a spiral search strategy, Lévy flight, and an adaptive t-distribution mutation strategy to prevent premature convergence. The model was validated using a dataset from the West–East Gas Pipeline, with 90% of the data being used for training and 10% for testing. The results demonstrate the superior performance of FA-IDBO-KELM, which achieved a root mean square error (RMSE) of 0.0028, a mean absolute error (MAE) of 0.0021, and a coefficient of determination (R²) of 0.9954 on the test set. Compared to benchmark models (FA-KELM, FA-SSA-KELM, FA-DBO-KELM), the proposed model reduced the RMSE by 93.0%, 89.1%, and 85.3%, and improved the R² by 85.7%, 10.6%, and 7.4%, respectively. The FA-IDBO-KELM model provides a highly accurate and reliable tool for predicting the external corrosion rate, which can significantly support pipeline maintenance decision-making. Full article

To address the issues of insensitivity to high-impedance ground faults and difficulty in identifying reflected wavefronts in single-ended traveling-wave fault location methods for asymmetric ground faults in high-voltage AC transmission lines, this paper proposes a single-ended fault location method based on the modal amplitude ratio and deep learning. First, based on the dispersion characteristics of traveling waves, an approximate formula is derived between the fault distance and the amplitude ratio of the sum of the initial transient voltage traveling-wave 1-mode and 2-mode to 0-mode at the measurement point. Simulation verifies that the fault distance x from the measurement point at the line head is unaffected by transition resistance and fault inception angle, and that a nonlinear positive correlation exists between the distance x and the modal amplitude ratio. The multi-scale wavelet modal maximum ratio of the sum of 1-mode and 2-mode to 0-mode is used to characterize the amplitude ratio. This ratio serves as the input for a Residual Bidirectional Long Short-Term Memory (BiLSTM) network, which is optimized using the Dung Beetle Optimizer (DBO). The DBO-Res-BiLSTM model fits the nonlinear mapping between the fault distance x and the amplitude ratio. Simulation results demonstrate that the proposed method achieves high location accuracy. Furthermore, it remains robust against variations in fault type, location, transition resistance, and inception angle. Full article