Search Results (251)

With the rapid development of radio technology and its widespread application in the military field, the electromagnetic environment in which radar communication operates is becoming increasingly complex. Among them, human radio interference makes radar countermeasures increasingly fierce. This requires radar systems to have strong capabilities in resisting electronic interference, anti-radiation missiles, and radar detection. However, array antennas are one of the effective means to solve these problems. In recent years, array antennas have been extensively utilized in various fields, including radar, sonar, and wireless communication. Many evolutionary algorithms have been employed to optimize the size and phase of array elements, as well as adjust the spacing between them, to achieve the desired antenna pattern. The main objective is to enhance useful signals while suppressing interference signals. In this paper, we introduce the dandelion optimization (DO) algorithm, a newly developed swarm intelligence optimization algorithm that simulates the growth and reproduction of natural dandelions. To address the issues of low precision and slow convergence of the DO algorithm, we propose an improved version called the chaos exchange nonlinear dandelion optimization (CENDO) algorithm. The CENDO algorithm aims to optimize the spacing of antenna array elements in order to achieve a low sidelobe level (SLL) and deep nulls antenna pattern. In order to test the performance of the CENDO algorithm in solving the problem of comprehensive optimization of non-equidistant antenna array patterns, five experimental simulation examples are conducted. In Experiment Simulation Example 1, Experiment Simulation Example 2, and Experiment Simulation Example 3, the optimization objective is to reduce the SLL of non-equidistant arrays. The CENDO algorithm is compared with DO, particle swarm optimization (PSO), the quadratic penalty function method (QPM), based on hybrid particle swarm optimization and the gravity search algorithm (PSOGSA), the whale optimization algorithm (WOA), the grasshopper optimization algorithm (GOA), the sparrow search algorithm (SSA), the multi-objective sparrow search optimization algorithm (MSSA), the runner-root algorithm (RRA), and the cat swarm optimization (CSO) algorithms. In the three examples above, the SLLs obtained using the CENDO algorithm optimization are all the lowest. The above three examples all demonstrate that the improved CENDO algorithm performs better in reducing the SLL of non-equidistant antenna arrays. In Experiment Simulation Example 4 and In Experiment Simulation Example 5, the optimization objective is to reduce the SLL of a non-uniform array and generate some deep nulls in a specified direction. The CENDO algorithm is compared with the DO algorithm, PSO algorithm, CSO algorithm, pelican optimization algorithm (POA), and grey wolf optimizer (GWO) algorithm. In the two examples above, optimizing the antenna array using the CENDO algorithm not only results in the lowest SLL but also in the deepest zeros. The above examples both demonstrate that the improved CENDO algorithm has better optimization performance in simultaneously reducing the SLL of non-equidistant antenna arrays and reducing the null depth problem. In summary, the simulation results of five experiments show that the CENDO algorithm has better optimization ability in the comprehensive optimization problem of non-equidistant antenna array patterns than all the algorithms compared above. Therefore, it can be regarded as a strong candidate to solve problems in the field of electromagnetism. Full article

In this study, an Improved Zebra Optimization Algorithm (ZOA) is proposed based on the search mechanism of the Sparrow Optimization Algorithm (SSA), the perturbation mechanism of the Particle Swarm Algorithm (PSO), and the adaptive function. Then, Improved Zebra Optimization Algorithm (IZOA) was used to optimize the Deep Hybrid Kernel Extreme Learning Machine Model (DHKELM), and the IZOA-DHKELM was obtained. The model has been used to predict the color of heat-treated wood for different species, temperatures, times, media, and profile types. In this article, the original DHKELM and the ZOA-DHKELM were compared to verify the validity and accuracy of the model. The results indicated that the IZOA-DHKELM decreased the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) by 56.2%, 67.4%, and 34.2%, respectively, while enhancing the coefficient of determination, $R^2$, to 0.9952 compared to the ZOA-DHKELM. This demonstrated that the model was significantly optimized, with improved generalization ability and prediction accuracy. It can better meet the actual engineering needs. Full article

Orthogonal time–frequency space (OTFS) modulation combined with massive multiple-input multiple-output (MIMO) can simultaneously address the problems caused by multipath delay, the Doppler effect, and channel fading. To mitigate inter-subcarrier and inter-symbol interference in satellite–terrestrial MIMO-OTFS systems, an iterative maximum ratio combining detection algorithm based on hard-decision interference cancelation (ICH-IMRC) is proposed. The signal detection is iterated by performing MRC on the interference-canceled received symbols. To mitigate the error spread in the interference cancelation process, iterative maximum ratio combining detection based on soft symbol interference cancelation (S-IMRC) is proposed, which is improved based on ICH-IMRC. The interference cancelation is updated by the expectation of other symbols, and the expectation and variance of symbols are updated by soft judgment with the posterior probability of symbols. To improve the detection convergence speed, optimal relaxation parameters are obtained based on the Sparrow Search Algorithm (SSA). Simulation results show that the proposed S-IMRC has superior error rate performance compared to the conventional algorithms for satellite–terrestrial MIMO-OTFS systems. Furthermore, the proposed algorithm is applicable to various satellite channel models and achieves excellent BER for different orders of orthogonal amplitude-modulated signals and different antenna array sizes. Full article

Industrial robots can cause servo system instability during operation due to friction between joints and changes in end loads, which results in jittering of the robotic arm. Therefore, this paper proposes a hybrid sparrow search algorithm (HSSA) method for PID parameter optimization. By studying the optimization characteristics of the genetic algorithm (GA) and sparrow search algorithm (SSA), the method combines the global optimization ability of GA and the local optimization ability of SSA, thus effectively reducing the risk of SSA falling into local optimum and improving the ability of SSA to find global optimization solutions. On the basis of the traditional PID control algorithm, HSSA is used to intelligently optimize the PID parameters so that it can better meet the nonlinear motion of the industrial robot servo system. It is proven through experiments that the HSSA in this paper, compared with GA, SSA, and traditional PID, has a maximum improvement of 73% in the step response time and a maximum improvement of more than 95% in the iterative optimization search speed. The experimental results show that the method has a good suppression effect on the jitter generated by industrial robots in motion, effectively improving the stability of the servo system, so this work greatly improves the stability and safety of industrial robots in operation. Full article

Accurate remaining capacity prediction (RCP) of lithium-ion batteries (LIBs) is crucial for ensuring their safety, reliability, and performance, particularly amidst the growing energy crisis and environmental concerns. However, the complex aging processes of LIBs significantly hinder accurate RCP, as traditional prediction methods struggle to effectively capture nonlinear degradation patterns and long-term dependencies. To tackle these challenges, we introduce an innovative framework that combines evolutionary learning with deep learning for RCP. This framework integrates Temporal Convolutional Networks (TCNs), Bidirectional Gated Recurrent Units (BiGRUs), and an attention mechanism to extract comprehensive time-series features and improve prediction accuracy. Additionally, we introduce a hybrid optimization algorithm that combines the Sparrow Search Algorithm (SSA) with Bayesian Optimization (BO) to enhance the performance of the model. The experimental results validate the superiority of our framework, demonstrating its capability to achieve significantly improved prediction accuracy compared to existing methods. This study provides researchers in battery management systems, electric vehicles, and renewable energy storage with a reliable tool for optimizing lithium-ion battery performance, enhancing system reliability, and addressing the challenges of the new energy industry. Full article

In order to address the impact of the uncertainty and intermittency of a photovoltaic power generation system on the smooth operation of the power system, a microgrid scheduling model incorporating photovoltaic power generation forecast is proposed in this paper. Firstly, the factors affecting the accuracy of photovoltaic power generation prediction are analyzed by classifying the photovoltaic power generation data using cluster analysis, analyzing its important features using Pearson correlation coefficients, and downscaling the high-dimensional data using PCA. And based on the theories of the sparrow search algorithm, convolutional neural network, and bidirectional long- and short-term memory network, a combined SSA-CNN-BiLSTM prediction model is established, and the attention mechanism is used to improve the prediction accuracy. Secondly, a multi-temporal dispatch optimization model of the microgrid power system, which aims at the economic optimization of the system operation cost and the minimization of the environmental cost, is constructed based on the prediction results. Further, differential evolution is introduced into the QPSO algorithm and the model is solved using this improved quantum particle swarm optimization algorithm. Finally, the feasibility of the photovoltaic power generation forecasting model and the microgrid power system dispatch optimization model, as well as the validity of the solution algorithms, are verified through real case simulation experiments. The results show that the model in this paper has high prediction accuracy. In terms of scheduling strategy, the generation method with the lowest cost is selected to obtain an effective way to interact with the main grid and realize the stable and economically optimized scheduling of the microgrid system. Full article

The accurate prediction of PM10 concentrations at smart construction sites is crucial for improving urban air quality, protecting public health, and advancing sustainable development in the construction industry. PM10 concentrations at construction sites are influenced by the interaction of construction intensity and environmental meteorological factors, resulting in nonlinear and volatile data. To improve prediction accuracy, this paper presents a two-stage mode decomposition method that integrates Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD). This method is combined with a Bidirectional Long Short-Term Memory (BiLSTM) neural network, optimized using the Sparrow Search Algorithm (SSA), to establish a hybrid model for forecasting PM10 concentrations at construction sites. Initially, CEEMDAN decomposes the original sequence into several Intrinsic Mode Functions (IMFs). The sample entropy of each component is then calculated, and K-means clustering is used to group them. VMD is applied to further decompose the high-frequency components obtained after clustering. SSA is then employed to optimize the parameters of the BiLSTM network, which models all the components with the optimized predictive model. The predicted values of all components are aggregated to generate the final forecast. Real-time monitoring data from Construction Site A in Nanjing are used for case study validation. The empirical results demonstrate that the proposed hybrid prediction model outperforms comparison models on all evaluation metrics, offering a scientific foundation for sustainable and automated dust reduction decision-making at smart construction sites, thereby facilitating the shift toward greener, smarter, and more digitized construction practices. Full article

The Jianghan Plain (JHP) is a key agricultural area in China where efficient agricultural water use (AWUE) is vital for sustainable water management, food security, environmental sustainability, and economic growth. This study introduces a novel AWUE prediction model for the JHP, combining a BP neural network with the Sparrow Search Algorithm (SSA) and an improved Tent Mixing Algorithm (Tent-SSA-BPNN). This hybrid model addresses the limitations of traditional methods by enhancing AWUE forecast accuracy and stability. By integrating historical AWUE data and environmental factors, the model provides a detailed understanding of AWUE’s spatial and temporal variations. Compared to traditional BP neural networks and other methods, the Tent-SSA-BPNN model significantly improves prediction accuracy and stability, achieving an accuracy (ACC) of 96.218%, a root mean square error (RMSE) of 0.952, and a coefficient of determination (R²) of 0.9939, surpassing previous models. The results show that (1) from 2010 to 2022, the average AWUE in the JHP fluctuated within a specific range, exhibiting a decrease of 0.69%, with significant differences in the spatial and temporal distributions across various cities; (2) the accuracy (ACC) of the Tent-SSA-BPNN prediction model was 96.218%, the root mean square error (RMSE) was 0.952, and the coefficient of determination (R²) value was 0.9939. (3) Compared with those of the preoptimization model, the ACC, RMSE, and R² values of the Tent-SSA-BPNN model significantly improved in terms of accuracy and stability, clearly indicating the efficacy of the optimization. (4) The prediction results reveal that the proportion of agricultural water consumption has a significant impact on AWUE. These results provide actionable insights for optimizing water resource allocation, particularly in water-scarce regions, and guide policymakers in enhancing agricultural water management strategies, supporting sustainable agricultural development. Full article

This paper presents a cable defect assessment method based on a mixed-domain multi-feature set derived from overall harmonic signals. Four typical defect types—thermal ageing, cable moisture, excessive bending, and insulation damage—were simulated under laboratory conditions. Grounding current tests and Variational Mode Decomposition (VMD) time series analysis were performed on the test samples to extract the overall harmonic sequences in the grounding current. Mixed-domain multi-feature set is then formed through feature extraction and validity analysis. To optimize the assessment performance, a Support Vector Machine (SVM) classifier optimized by the Sparrow Search Algorithm (SSA) was constructed. The results show that different defects lead to significantly differentiated harmonic distortions in the grounding currents, which has proved to be a reliable data basis for cable defect assessment. The proposed method refines the data information and achieves the most accurate recognition of cable defects, which may contribute to the reliable operation of power networks. Full article

The main shaft of a wind turbine is a critical component that ensures the normal operation of the turbine, and its axial displacement directly impacts its efficiency and safety. The inaccurate measurement of axial displacement may lead to severe issues such as shaft fractures, causing turbine shutdowns. Correcting measurement errors related to axial displacement is essential to prevent potential accidents. This study proposes an improved error correction method for measuring the axial displacement of wind turbine main shafts. Using a position-sensitive detector (PSD) and laser triangulation, the axial and radial displacements of the main shaft are measured to address environmental interference and cost constraints. Additionally, a Sparrow Search Algorithm- Backpropagation (SSA-BP) model is constructed based on operational data from the wind turbine’s main shaft to correct the system’s nonlinear errors. The Sparrow Search Algorithm (SSA) is employed to optimize the weights and thresholds of the Backpropagation (BP) neural network, enhancing prediction accuracy and model stability. Initially, a main shaft displacement measurement system based on a precision displacement stage was developed, and system stability tests and displacement measurement experiments were conducted. The experimental results demonstrate that the system stability error is ±0.025 mm, which is lower than the typical error of 0.05 mm in contact measurement. After model correction, the maximum nonlinear errors of the axial and radial displacement measurements are 0.83% and 1.29%, respectively, both of which are lower than the typical measurement error of 2% in contact measurements. This indicates that the proposed model can reliably and effectively correct the measurement errors. However, further research is still necessary to address potential limitations, such as its applicability in extreme environments and the complexity of implementation. Full article

The accurate prediction of tunneling speed in tunnel boring machine (TBM) construction is the basis for the timely adjustment of the operating parameters of TBM equipment to ensure safe and efficient tunneling. In this paper, a multi-step prediction model of TBM tunneling speed based on the EWT-ICEEMDAN-SSA-LSTM hybrid model is proposed. Firstly, four datasets were selected under different geological conditions, and the original data were preprocessed using the binary discriminant function and the $3\sigma$ principle; secondly, the preprocessed data were decomposed using the empirical wavelet variation (EWT) to obtain several subseries and residual series; then, Intrinsic Computing Expressive Empirical Mode Decomposition With Adaptive Noise (ICEEMDAN) was used to perform further decomposition on residual sequences. Finally, several subsequences were fed into a Long Short-Term Memory (LSTM) network optimized by the Sparrow Search Algorithm (SSA) for multi-step training and prediction, and the predicted results of each subsequence were added up to obtain the final result. A comparison with existing models showed that the performance of the prediction method proposed in this paper is superior to other models. Of the four datasets, the average accuracy from the first step prediction to the fifth step prediction reached 99.06%, 98.99%, 99.07%, and 99.03%, respectively, indicating that the proposed method has high multi-step prediction performance and generalization ability. In this sense, this paper provides a reference for other projects. Full article

This work proposes a new two-dimensional dynamical system with complete nonlinearity. This system inherits its nonlinearity from trigonometric and hyperbolic functions like sine, cosine, and hyperbolic sine functions. This system gives birth to infinite but countable coexisting attractors before and after being forced. These two megastable systems differ in the coexisting attractors’ type. Only limit cycles are possible in the autonomous version, but torus and chaotic attractors can emerge after transforming to the nonautonomous version. Because of the position of equilibrium points in different attractors’ attraction basins, this system can simultaneously exhibit self-excited and hidden coexisting attractors. This system’s dynamic behaviors are studied using state space, bifurcation diagram, Lyapunov exponents (LEs) spectrum, and attraction basins. Finally, the forcing term’s amplitude and frequency are unknown parameters that need to be found. The sparrow search algorithm (SSA) is used to estimate these parameters, and the cost function is designed based on the proposed system’s return map. The simulation results show this algorithm’s effectiveness in identifying and estimating parameters of the novel megastable chaotic system. Full article

To address the issues of slow convergence and large errors in existing metaheuristic algorithms when optimizing neural network-based subway passenger flow prediction, we propose the following improvements. First, we replace the random initialization method of the population in the SSA with Circle mapping to enhance its diversity and quality. Second, we introduce a hybrid mechanism combining dimensional small-hole imaging backward learning and Cauchy mutation, which improves the diversity of the individual sparrow selection of optimal positions and helps overcome the algorithm’s tendency to become trapped in local optima and premature convergence. Finally, we enhance the individual sparrow position update process by integrating a cosine strategy with an inertia weight adjustment, which improves the algorithm’s global search ability, effectively balancing global search and local exploitation, and reducing the risk of local optima and insufficient convergence precision. Based on the analysis of the correlation between different types of subway station passenger flows and weather factors, the ISSA is used to optimize the hyperparameters of the CNN-LSTM model to construct a subway passenger flow prediction model based on ISSA-CNN-LSTM. Simulation experiments were conducted using card swipe data from Harbin Metro Line 1. The results show that the ISSA provides a more accurate optimization with the average values and standard deviations of the 12 benchmark test function simulations being closer to the optimal values. The ISSA-CNN-LSTM model outperforms the SSA-CNN-LSTM, PSO-ELMAN, GA-BP, CNN-LSTM, and LSTM models in terms of error evaluation metrics such as MAE, RMSE, and MAPE, with improvements ranging from 189.8% to 374.6%, 190.9% to 389.5%, and 3.3% to 6.7%, respectively. Moreover, the ISSA-CNN-LSTM model exhibits the smallest variation in prediction errors across different types of subway stations. The ISSA demonstrates superior parameter optimization accuracy and convergence speed compared to the SSA. The ISSA-CNN-LSTM model is suitable for the precise prediction of passenger flow at different types of subway stations, providing theoretical and data support for subway station passenger density and trend forecasting, passenger organization and management, risk emergency response, and the improvement of service quality and operational safety. Full article

Accurate fault diagnosis of transformers is crucial for preventing power system failures and ensuring the continued reliability of electrical grids. To address the challenge of low accuracy in transformer fault diagnosis using support vector machines (SVMs), an enhanced fault diagnosis model is proposed, which utilizes an improved dung beetle optimization algorithm (IDBO) to optimize an SVM. First, based on dissolved gas analysis (DGA), five characteristic quantities are selected as input features. Second, improvements to the DBO algorithm are made by incorporating Chebyshev chaotic mapping, a golden sine strategy, and dynamic weight coefficients for position updates. The performance of the IDBO is validated using four benchmark test functions, demonstrating faster convergence. Subsequently, the IDBO optimizes the SVM’s penalty factor C and kernel function parameter g, which are then input into the SVM for training, yielding an efficient fault diagnosis model. Finally, comparisons with other methods confirm the usefulness of the proposed model. Experimental results demonstrate that the IDBO–SVM model attains accuracy improvements of 1.69%, 8.47%, and 10.17% over dung beetle optimization–SVM (DBO–SVM), sparrow search algorithm–SVM (SSA–SVM), and grey wolf optimization–SVM (GWO–SVM) models, respectively. In addition to higher accuracy, the IDBO–SVM model also delivers a faster runtime, further highlighting its superior performance in transformer fault diagnosis. The proposed model has practical significance for enhancing the stability of transformer operation. Full article

To address the problems of limited population diversity and a tendency to converge prematurely to local optima in the original sparrow search algorithm (SSA), an improved sparrow search algorithm (ISSA) based on multi-strategy collaborative optimization is proposed. ISSA employs three strategies to enhance performance: introducing one-dimensional composite chaotic mapping SPM to generate the initial sparrow population, thus enriching population diversity; introducing the dung beetle dancing search behavior strategy to strengthen the algorithm’s ability to jump out of local optima; integrating the adaptive t-variation improvement strategy to balance global exploration and local exploitation capabilities. Through experiments with 23 benchmark test functions and comparison with algorithms such as PSO, GWO, WOA, and SSA, the advantages of ISSA in convergence speed and optimization accuracy are verified. In the application of robot path planning, compared with SSA, ISSA exhibits shorter path lengths, fewer turnings, and higher planning efficiency in both single-target point and multi-target point path planning. Especially in multi-target point path planning, as the obstacle rate increases, ISSA can more effectively find the shortest path. Its traversal order is different from that of SSA, making the planned path smoother and with fewer intersections. The results show that ISSA has significant superiority in both algorithm performance and path planning applications. Full article