Search Results (83)

The Sparrow Search Algorithm (SSA), proposed by Jiankai Xue in 2020, is a swarm intelligence optimization algorithm that has received extensive attention due to its powerful optimization-seeking ability and rapid convergence. However, similar to other swarm intelligence algorithms, the SSA has the problem of being prone to falling into local optimal solutions during the optimization process, which limits its application effectiveness. To overcome this limitation, this paper proposes a Modified Sparrow Search Algorithm (MSSA), which enhances the algorithm’s performance by integrating three optimization strategies. Specifically, the Latin Hypercube Sampling (LHS) method is employed to achieve a uniform distribution of the initial population, laying a solid foundation for global search. An adaptive weighting mechanism is introduced in the producer update phase to dynamically adjust the search step size, effectively reducing the risk of the algorithm falling into local optima in later iterations. Meanwhile, the cat mapping perturbation and Cauchy mutation operations are integrated to further enhance the algorithm’s global exploration ability and local development efficiency, accelerating the convergence process and improving the quality of the solutions. This study systematically validates the performance of the MSSA through multi-dimensional experiments. The MSSA demonstrates excellent optimization performance on 23 benchmark test functions and the CEC2019 standard test function set. Its application to three practical engineering problems, namely the design of welded beams, reducers, and cantilever beams, successfully verifies the effectiveness of the algorithm in real-world scenarios. By comparing it with deterministic algorithms such as DIRET and BIRMIN, and based on the five-dimensional test functions generated by the GKLS generator, the global optimization ability of the MSSA is thoroughly evaluated. In addition, the successful application of the MSSA to the problem of robot path planning further highlights its application advantages in complex practical scenarios. Experimental results show that, compared with the original SSA, the MSSA has achieved significant improvements in terms of convergence speed, optimization accuracy, and robustness, providing new ideas and methods for the research and practical application of swarm intelligence optimization algorithms. Full article

To improve the global search efficiency and dynamic adaptability of the Sand Cat Swarm Optimization (SCSO) algorithm for UAV path planning in complex 3D environments, this study proposes a Modified Sand Cat Swarm Optimization (MSCSO) algorithm by integrating chaotic mapping initialization, Lévy flight–Metropolis hybrid exploration mechanisms, simulated annealing–particle swarm hybrid exploitation strategies, and elite mutation techniques. These strategies not only significantly enhance the convergence speed while ensuring algorithmic precision but also provide effective avenues for enhancing the performance of SCSO. We successfully apply these modifications to UAV path planning scenarios in complex environments. Experimental results on 18 benchmark functions demonstrate the enhanced convergence speed and stability of MSCSO. The proposed method has a superior performance in multimodal optimization tasks. The performance of MSCSO in eight complex scenarios that derived from real-world terrain data by comparing MSCSO with three state-of-the-art algorithms, MSCSO generates shorter average path lengths, reduces collision risks by 21–35%, and achieves higher computational efficiency. Its robustness in obstacle-dense and multi-waypoint environments confirms its practicality in engineering contexts. Overall, MSCSO demonstrates substantial potential in low-altitude resource exploration and emergency rescue operations. These innovative strategies offer theoretical and technical foundations for autonomous decision-making in intelligent unmanned systems. Full article

This paper represents a new method for the extraction of features from 5G signals using spectrogram and quantum cat swarm optimization (QCSO). The proposed approach uses a discrete wavelet transform (DWT)-based convolutional neural network (W-CNN) to enhance the extracted features and improve the signal classification. The combination of QCSO and W-CNN is designed to enable improved signal recognition and dimension reduction. Our results demonstrate an improvement in the 5G signal feature extraction performance with the use of this novel approach. The QCSO shows improvement in seven out of eight parameters studied when compared to five other state-of-the-art optimization methods. Full article

The vertical inline pump, a single-stage centrifugal pump with a bent elbow inlet, is widely used in marine engineering. The unique water inlet passage combined with uneven inflow at the impeller inlet tends to form an inlet vortex and secondary flow area, which reduces performance and causes vibration. To predict the performance of the elbow inline pump, this study uses spline curve fitting for the centerline and cross-sectional shape of the elbow passage. With four elbow inlet variables from experimental design as the input layer and targeting efficiency under pump operating conditions, a pump performance prediction model based on an improved sand cat swarm optimization algorithm combined with a BP neural network (MSCSO-BP) is proposed. Six test functions are used to effectively test the improved sand cat swarm optimization algorithm. The results show that compared to the unimproved algorithm, the improved algorithm has significantly faster convergence speed, shorter parameter optimization time, and higher accuracy. For more demanding multidimensional test functions, the improved optimization algorithm can more accurately find the optimal solution, enhancing the prediction accuracy and generalization ability of inline pump performance. This provides a more effective engineering solution for the design and optimization of inline pumps. Full article

This paper proposes an Enhanced Dynamic Sand Cat Search Optimization algorithm (EDSCSO) designed to address the high-order nonlinearities and strong coupling issues in the parameter tuning of the position sliding mode controller for electro-hydrostatic actuators (EHAs). Traditional swarm intelligence optimization algorithms often struggle with the transition from global to local search, which leads to being trapped in local optima and results in lower computational efficiency. To overcome these challenges, the EDSCSO algorithm introduces an escape mechanism, a stochastic elite cooperative bootstrap strategy, and a multi-path differential perturbation strategy. These enhancements significantly increase the diversity of the population, facilitate a smooth transition from global to local search, avoid local optimum traps, and better balance the exploration and exploitation capabilities of the algorithm. Based on this algorithm, the sliding mode surface and convergence rate parameters within the sliding mode controller are optimized. Simulation validations conducted on the combined platform of MATLAB/Simulink and AMESim demonstrate that the sliding mode PID controller optimized by the EDSCSO algorithm achieves smaller steady-state and tracking errors, exhibits greater robustness, and offers enhanced computational efficiency compared to other swarm intelligence optimization algorithms. This study provides an effective optimization strategy to improve the control performance of the EHA position sliding mode controller. Full article

The state of charge (SOC) of lithium-ion batteries is essential for their proper functioning and serves as the basis for estimating other parameters within the battery management system. To enhance the accuracy of SOC estimation in lithium-ion batteries, we propose a joint estimation method that integrates lithium-ion battery parameter identification and SOC assessment using cat swarm optimization dual Kalman filtering (CSO–DKF), which accounts for variable-temperature conditions. We adopt a second-order equivalent circuit model, utilizing the Kalman filtering (KF) algorithm as a parameter filter for dynamic parameter identification, while the extended Kalman filtering (EKF) algorithm acts as a state filter for real-time SOC estimation. These two filters operate alternately throughout the iterative process. Additionally, the cat swarm optimization (CSO) algorithm optimizes the noise covariance matrices of both filters, thereby enhancing the precision of parameter identification and SOC estimation. To support this algorithm, we establish an environmental temperature battery database and incorporate temperature variables to achieve accurate SOC estimation under variable-temperature conditions. The results indicate that creating a database that accommodates temperature variations and optimizing dual Kalman filtering through the cat swarm optimization algorithm significantly improves SOC estimation accuracy. Full article

The surface settlement of railroad tunnels is dynamically updated as the construction progresses, exhibiting complex nonlinear characteristics. The accuracy of the on-site nonlinear regression fitting prediction method needs to be improved. To prevent surface settlement and surrounding rock collapse during railroad tunnel construction, while also ensuring the safety of the tunnel and existing structures, we propose a recursive prediction model for the long-term trend of surface settlement utilizing a singular spectrum analysis (SSA), improved sand cat swarm optimization (ISCSO), and a kernel extreme learning machine (KELM). First, SSA decomposition, known for its adaptive decomposition of one-dimensional nonlinear time series, reorganizes the early surface settlement data. The dynamic sliding window method is introduced to construct the prediction dataset, which is then trained using the KELM. ISCSO is used to optimize the key parameters of the KELM to obtain the long-term trend curves of surface settlement through recursive time series prediction. The superiority and effectiveness of ISCSO and the model are verified through numerical experiments and simulation experiments based on engineering cases, providing a reference for the early warning and control of surface settlement during the construction of similar tunnels. Full article

Amid the rapid growth of the new energy vehicle industry and the accelerating global shift toward green and low-carbon energy alternatives, this paper develops a multi-objective optimization model for an Electric Vehicle Integrated Energy Station (EVIES) and a high-rise building wind-solar-storage sharing system. The model aims to maximize the daily economic revenue of the EVIES, minimize the load variance on the grid side of the building, and reduce overall carbon emissions. To solve this multi-objective optimization problem, a Multi-Objective Sand Cat Swarm Optimization Algorithm (MSCSO) based on a mutation-dominated selection strategy is proposed. Benchmark tests confirm the significant performance advantages of MSCSO in both solution quality and stability, achieving the optimal mean and minimum variance in 73% of the test cases. Further comparative analyses validate the effectiveness of the proposed system, showing that the optimized configuration increases daily economic revenue by 26.54% on average and reduces carbon emissions by 37.59%. Additionally, post-optimization analysis reveals a smoother load curve after grid integration, a significantly reduced peak-to-valley difference, and improved overall operational stability. Full article

Power system stability is managed through various control loops, including the Automatic Voltage Regulator (AVR), which regulates the terminal voltage of synchronous generators. This study integrated Fuzzy Logic Control (FLC) and a Proportional–Integral–Derivative controller with Filtered derivative action (PIDF) to propose a hybrid Fuzzy PIDF controller enhanced by Fractional-Order Proportional-Derivative (FOPD) for AVR applications. For the first time, the newly introduced Sand Cat Swarm Optimization (SCSO) algorithm was applied to the AVR system to tune the parameters of the proposed fuzzy controller. The SCSO algorithm has been recognized as a powerful optimization tool and has demonstrated success across various engineering applications. The well-known Particle Swarm Optimization (PSO) algorithm was also utilized in this study to optimize the gains of the proposed controller. The Fuzzy PIDF plus FOPD is a novel configuration that is designed to be a robust control technique for AVR to achieve an excellent performance. In this research, the Fuzzy PIDF + FOPD controller was optimized using the PSO and SCSO algorithms by minimizing the Integral Time Absolute Error (ITAE) objective function to enhance the overall performance of AVR systems. A comparative analysis was conducted to evaluate the superiority of the proposed approach by benchmarking the results against those of other controllers reported in the literature. Furthermore, the robustness of the controller was assessed under parametric uncertainties and varying load disturbances. Also, its robustness was examined against disturbances in the control signal. The results demonstrate that the proposed Fuzzy PIDF + FOPD controller tuned by the PSO and SCSO algorithms delivers exceptional performance as an AVR controller, outperforming other controllers. Additionally, the findings confirm the robustness of the Fuzzy PIDF + FOPD controller against parametric uncertainties, establishing its potential for a successful implementation in real-time applications. Full article

Carbon fiber composites (CFRPs) are prone to impact loads during their production, transportation, and service life. These impacts can induce microscopic damage that is always undetectable to the naked eye, thereby posing a significant safety risk to the structural integrity of CFRP structures. In this study, we developed an impact localization system for CFRP structures using extrinsic Fabry–Perot interferometric (EFPI) sensors. The impact signals detected by EFPI sensors are demodulated at high speeds using an intensity modulation method. An impact localization method for the CFRP structure based on the energy–entropy ratio endpoint detection and CNN-BIGRU-Attention is proposed. The time difference of arrival (TDOA) between signals from different EFPI sensors is collected to characterize the impact location. The attention mechanism is integrated into the CNN-BIGRU model to enhance the significance of the TDOA of impact signals detected by proximal EFPI sensors. The model is trained using the training set, with its parameters optimized using the sand cat swarm optimization algorithm and validation set. The localization performance of different models is then evaluated and compared using the test set. The impact localization system based on the CNN-BIGRU-Attention model using EFPI sensors was validated on a CFRP plate with an experimental area of 400 mm × 400 mm. The average error in impact localization is 8.14 mm, and the experimental results demonstrate the effectiveness and satisfactory performance of the proposed method. Full article

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

The continuous and effective monitoring of the water quality of small rural rivers is crucial for rural sustainable development. In this work, machine learning models were established to predict the water quality of a typical small rural river based on a small quantity of measured water quality data and UAV hyperspectral images. Firstly, the spectral data were preprocessed using fractional order derivation (FOD), standard normal variate (SNV), and normalization (Norm) to enhance the spectral response characteristics of the water quality parameters. Second, a method combining the Pearson’s correlation coefficient and the variance inflation factor (PCC–VIF) was utilized to decrease the dimensionality of features and improve the quality of the input data. Again, based on the screened features, a back-propagation neural network (BPNN) model optimized using a mixture of the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm was established as a means of estimating water quality parameter concentrations. To intuitively evaluate the performance of the hybrid optimization algorithm, its prediction accuracy is compared with that of conventional machine learning algorithms (Random Forest, CatBoost, XGBoost, BPNN, GA–BPNN and PSO–BPNN). The results show that the GA–PSO–BPNN model for turbidity (TUB), ammonia nitrogen (NH₃-N), total nitrogen (TN), and total phosphorus (TP) prediction exhibited optimal accuracy with coefficients of determination (R²) of 0.770, 0.804, 0.754, and 0.808, respectively. Meanwhile, the model also demonstrated good robustness and generalization ability for data from different periods. In addition, we used this method to visualize the water quality parameters in the study area. This work provides a new approach to the refined monitoring of water quality in small rural rivers. Full article

Landslides induced by heavy rainfall are common in southern China and pose significant risks to the safe operation of transmission lines. To ensure the reliability of transmission line operations, this paper presents a stability prediction model for transmission tower slopes based on the Improved Sand Cat Swarm Optimization (ISCSO) algorithm and Support Vector Machine (SVM). The ISCSO algorithm is enhanced with dynamic reverse learning and triangular wandering strategies, which are then used to optimize the kernel and penalty parameters of the SVM, resulting in the ISCSO-SVM prediction model. In this study, a typical transmission tower slope in southern China is used as a case study, with the transmission tower slope database generated through orthogonal experimental design and Geo-studio simulations. In addition to traditional input features, an additional input—transmission tower catchment area—is incorporated, and the stable state of the transmission tower slope is set as the predicted output. The results demonstrate that the ISCSO-SVM model achieves the highest prediction accuracy, with the smallest errors across all metrics. Specifically, compared to the standard SVM, the MAPE, MAE, and RMSE values are reduced by 70.96%, 71.41%, and 57.37%, respectively. The ISCSO-SVM model effectively predicts the stability of transmission tower slopes, thereby ensuring the safe operation of transmission lines. Full article

As the number of intelligent devices in mining environments increases, the transmission time for large datasets, including equipment status and environmental parameters, also rises. This increase leads to longer response times for service requests, making it difficult to meet the equipment’s real-time requirements. Edge computing effectively addresses the demands for low latency and high performance. However, the deployment of edge nodes can negatively affect overall service performance due to resource limitations and node heterogeneity. In this paper, we propose two node deployment strategies: an improved genetic algorithm (IBGA) for fixed device scenarios and an improved sand cat swarm optimization algorithm (ISCSO) for mobile device scenarios, both accounting for the mobility characteristics of the devices. Additionally, we developed a simulation platform based on a production line system and an intelligent patrol vehicle to evaluate the proposed method’s effectiveness. The experimental results show that the IBGA and ISCSO algorithms effectively reduce task delay and deployment cost. Both deployment methods outperform the benchmark algorithms and offer better service quality assurance. Full article

Accurate prediction of concrete dam deformation is essential for ensuring structural safety and operational efficiency. This study presents a novel approach for monitoring and predicting concrete dam deformation using observed air temperature data, intelligent optimization, and machine learning techniques. To address the limitations of traditional statistical models in simulating the thermal effects on dam body deformation, this study proposes an improved hydraulic–air temperature–time (HT_airT) deformation monitoring model. This model leverages long-term air temperature data and its lagged terms as critical input variables, enabling a more comprehensive understanding of thermal impacts on dam deformation. To capture the complex, nonlinear relationships between environmental factors and dam deformation behavior, we introduce the high-performance CatBoost gradient-boosting algorithm as a regressor. An enhanced Particle Swarm Optimization (PSO) algorithm is utilized for optimizing CatBoost’s parameters, enhancing the model’s predictive accuracy. A high concrete dam, currently in service, is selected as the case study, where two representative deformation monitoring points are used for validation. This research fills a gap by combining CatBoost with an optimized PSO in a deformation monitoring model, providing a novel approach that improves predictive reliability in long-term dam safety monitoring. Experimental results show that the enhanced PSO-optimized CatBoost algorithm achieves higher R² and lower MSE and MAE values in multiple monitoring points. compared with other benchmark methods Moreover, the importance of factors affecting deformation can be identified using the proposed method, and experimental results indicate that water level and average air temperature of 1–2 days, 3–7 days, and 30–60 days are key factors affecting the deformation of high concrete arch dams. Full article