Search Results (43)

During the continuous operation of trailing suction hopper dredger (TSHD), equipment workload exhibits significant time-varying characteristics. Maintaining dynamic symmetry between power generation and consumption is crucial for ensuring system stability and preventing power supply failures. Key challenges lie in dynamic perception, accurate prediction, and real-time power management to achieve this equilibrium. To address this issue, this paper proposes and constructs a “prediction-driven dynamic power management method.” Firstly, to model the complex temporal dependencies of the workload sequence, we introduce and improve a dilated convolutional long short-term memory network (Dilated-LSTM) to build a workload prediction model with strong long-term dependency awareness. This model significantly improves the accuracy of workload trend prediction. Based on the accurate prediction results, a dynamic power management strategy is developed: when the predicted total power consumption is about to exceed a preset margin threshold, the Power Management System (PMS) automatically triggers power reduction operations for adjusfigure loads, aiming to maintain grid balance without interrupting critical loads. If the power that the generator can produce is still less than the required power after the power is reduced, and there is still a risk of supply-demand imbalance, the system uses an Improved Grey Wolf Optimization (IGWO) algorithm to automatically disconnect some non-critical loads, achieving real-time dynamic symmetry matching of generation capacity and load demand. Experimental results show that this mechanism effectively prevents generator overloads or ship-wide power failures, significantly improving system stability and the reliability of power supply to critical loads. The research results provide effective technical support for intelligent energy efficiency management and safe operation of TSHDs and other vessels with complex working conditions. Full article

Due to insufficient absolute positioning accuracy, industrial robots frequently face challenges in efficiently performing drilling and riveting operations during the assembly of aircraft and other large-scale workpieces. To enhance the absolute positioning accuracy of industrial robots, this paper proposes a novel kinematic calibration method for industrial robots based on the Improved Grey Wolf Optimization (IGWO) algorithm. Specifically, the method employs an enhanced selection and update strategy to avoid convergence stagnation and local optimum traps. The proposed method features a novel boundary search strategy, which leverages the Dimension-oriented Learning (DL) search strategy to enhance search speed and stability. Through parameter identification and calibration experiments, the effectiveness of the method was validated using an ABB IRB4600 industrial robot and a Leica laser tracker. Additionally, compared with the Levenberg–Marquardt (LM) algorithm, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), the IGWO algorithm demonstrates faster convergence and superior optimization performance. According to the calibration experimental results, by applying the IGWO algorithm, the absolute positioning accuracy of the industrial robot is ultimately improved from 1.918 mm to 0.475 mm and the absolute positioning accuracy is improved by 75.2%. Full article

Breast cancer (BRCA) remains a significant cause of mortality among women, particularly in developing and underdeveloped regions, where early detection is crucial for effective treatment. This research introduces an innovative hybrid model that combines Improved Grey Wolf Optimizer (IGWO) with the Seagull Optimization Algorithm (SOA), forming the IGWO–SOA technique to enhance BRCA detection accuracy. The hybrid model draws inspiration from the adaptive and strategic behaviors of seagulls, especially their ability to dynamically change attack angles in order to effectively tackle complex global optimization challenges. A deep neural network (DNN) is fine-tuned using this hybrid optimization method to address the challenges of hyperparameter selection and overfitting, which are common in DL approaches for BRCA classification. The proposed IGWO–SOA model demonstrates optimal performance in identifying key attributes that contribute to accurate cancer detection using the CBIS-DDSM dataset. Its effectiveness is validated using performance metrics such as loss, F1-score, precision, accuracy, and recall. Notably, the model achieved an impressive accuracy of 99.4%, outperforming existing methods in the domain. By optimizing both the learning parameters and model structure, this research establishes an advanced deep learning framework built upon the IGWO–SOA approach, presenting a robust and reliable method for early BRCA detection with significant potential to improve diagnostic precision. Full article

In the domain of financial markets, deep learning techniques have emerged as a significant tool for the development of investment strategies. The present study investigates the potential of time series forecasting (TSF) in financial application scenarios, aiming to predict future spreads and inform investment decisions more effectively. However, the inherent nonlinearity and high volatility of financial time series pose significant challenges for accurate forecasting. To address these issues, this paper proposes the IGWO-MALSTM model, a hybrid framework that integrates Improved Grey Wolf Optimization (IGWO) for hyperparameter tuning and a multi-head attention (MA) mechanism to enhance long-term sequence modeling within the long short-term memory (LSTM) architecture. The IGWO algorithm improves population diversity during initialization using the Mersenne Twister, thereby enhancing the convergence speed and search capability of the optimizer. Simultaneously, the MA mechanism mitigates gradient vanishing and explosion problems, enabling the model to better capture long-range dependencies in financial sequences. Experimental results on real futures market data demonstrate that the proposed model reduces Mean Square Error (MSE) by up to 61.45% and Mean Absolute Error (MAE) by 44.53%, and increases the R² score by 0.83% compared to existing benchmark models. These findings confirm that IGWO-MALSTM offers improved predictive accuracy and stability for financial time series forecasting tasks. Full article

The Grey Wolf Optimizer (GWO) is a well-known metaheuristic algorithm that currently has an extremely wide range of applications. However, with the increasing demand for accuracy, its shortcomings of low exploratory and population diversity are increasingly exposed. A modified Grey Wolf Optimizer (M-GWO) is proposed to tackle these weaknesses of the GWO. The M-GWO introduces mutation operators and different location-update strategies, achieving a balance between exploration and development. The experiment validated the performance of the M-GWO using the CEC2017 benchmark function and compared the results with five other advanced metaheuristic algorithms: the Improved Grey Wolf Optimizer (IGWO), GWO, Whale Optimization Algorithm (WOA), Dung Beetle Optimizer (DBO), and Harris Hawks Optimization (HHO). The performance results indicate that the M-GWO has a better performance than competitor algorithms on all 29 functions in dimensions 30 and 50, except for function 26 in dimension 30 and function 28 in dimension 50. Compared with competitor algorithms, the proposed M-GWO is the most effective algorithm, with an overall effectiveness of 96.5%. In addition, in order to show the value of the M-GWO in the practical engineering field, the M-GWO is used to optimize the PI controller parameters of the current loop of the permanent magnet synchronous motor (PMSM) system. By designing a PI controller parameter optimization scheme based on the M-GWO, the fluctuation of the q-axis current and d-axis current of the motor is reduced. The designed scheme reduces the q-axis fluctuation to around −2~1 A and the d-axis current fluctuation to around −2~2 A. By comparing the current-tracking errors of the $q$-axis and $d$-axis under different algorithms, the validity of the optimized parameters of the M-GWO is proved. Full article

This study aims to optimize the turning parameters for EN-GJL-250 grey cast iron using hybrid machine learning techniques integrated with multi-objective optimization algorithms. The experimental design focused on evaluating the impact of cutting tool type, testing three tools: uncoated and coated silicon nitride (Si₃N₄) ceramic inserts and coated cubic boron nitride (CBN). Key cutting parameters such as depth of cut (ap), feed rate (f), and cutting speed (Vc) were varied to examine their effects on surface roughness (Ra), cutting force (Fr), and power consumption (Pc). The results showed that the coated Si₃N₄ tool achieved the best surface finish, with minimal cutting force and power consumption, while the uncoated Si₃N₄ and CBN tools performed slightly worse. Advanced optimization models including improved grey wolf optimizer–deep neural networks (DNN-IGWOs), genetic algorithm–deep neural networks (DNN-GAs), and deep neural network–extended Kalman filters (DNN-EKF) were compared with traditional methods like Support Vector Machines (SVMs), Decision Trees (DTs), and Levenberg–Marquardt (LM). The DNN-EKF model demonstrated exceptional predictive accuracy with an R² value of 0.99. The desirability function (DF) method identified the optimal machining parameters for the coated Si₃N₄ tool: ap = 0.25 mm, f = 0.08 mm/rev, and Vc = 437.76 m/min. At these settings, Fr ranged between 46.424 and 47.405 N, Ra remained around 0.520 µm, and Pc varied between 386.518 W and 392.412 W. The multi-objective grey wolf optimization (MOGWO) further refined these parameters to minimize Fr, Ra, and Pc. This study demonstrates the potential of integrating machine learning and optimization techniques to significantly enhance manufacturing efficiency. Full article

To address the challenge of the real-time monitoring of alumina concentrations during the production process, this paper employs a Deep Belief Network (DBN) within the framework of deep learning to predict alumina concentration. Additionally, the improved Grey Wolf Optimizer (IGWO) is utilized to optimize key parameters of the DBN model, including the number of hidden layer nodes, reverse iteration count, and learning rate. An IGWO-DBN hybrid model is then constructed and compared against DBN models optimized by other techniques, such as the Sparrow Search Algorithm (SSA) and Particle Swarm Optimization (PSO), to evaluate the predictive performance. The comparative analysis reveals that, in terms of predictive accuracy, the IGWO-DBN model outperforms both the SSA-DBN and PSO-DBN models. Specifically, it achieves lower root mean square errors (RMSE) and mean absolute errors (MAE), alongside a higher coefficient of determination (R²). Furthermore, the IGWO-DBN model exhibits a faster convergence rate and a lower final convergence value, indicating superior generalization ability and robustness. Furthermore, the IGWO-DBN model not only demonstrates significant advantages in prediction accuracy for alumina concentration but also substantially reduces model training time through its efficient parameter optimization mechanism. The successful implementation of this model provides robust support for the intelligent and refined management of the aluminum electrolysis industry, aiding enterprises in reducing costs, improving production efficiency, and advancing the green and sustainable development of the industry. Full article

Accurately determining the mechanical parameters of surrounding rock in tunnel design and construction presents a significant challenge due to the complexity of the environment. This study proposes a novel approach for inverting these parameters using an advanced optimization method, the Improved Grey Wolf Optimization (IGWO), integrated with a BP neural network (IGWO-BP). Key enhancements such as cubic chaotic mapping, refraction backward learning, nonlinear convergence factors, and updated position formulas were applied to improve the algorithm’s search efficiency. By optimizing the neural network’s weights and biases, a precise relationship between rock mechanics and displacement was established. The method was validated through a case study of the Lianhua Tunnel (YK37 + 330 section), utilizing field data of crown settlement and peripheral displacement. The approach accurately predicted mechanical parameters, with relative errors below 5.02% for crown settlement and 4.15% for peripheral displacement. These results demonstrate the reliability and practical applicability of the proposed technique for tunnel engineering. Full article

This study proposes a high-precision method for retrieving sea surface salinity (SSS) using GF-1 satellite imagery, focusing on Qinzhou Bay along the Guangxi coast. The analysis identified the spectral index B3×B4/(B1×B2) as having the strongest correlation with SSS (R = 0.929). To enhance the performance of the Back Propagation Neural Network (BPNN) model, optimization algorithms including Improved Grey Wolf Optimization (IGWO), Particle Swarm Optimization (PSO), and White Shark Optimization (WSO) were applied. Comparative results show that IGWO significantly optimized network weights and thresholds, yielding superior test performance metrics (MAE = 0.906 psu, MAPE = 4.124%, RMSE = 1.067 psu, and R² = 0.953), demonstrating strong generalization ability. Validation using third-party data indicated accuracy reductions of 10.9% and 8.6% in Qinzhou Bay and Tieshan Port, respectively, highlighting the model’s robustness and broad applicability. SSS retrieval results for Qinzhou Bay in 2023 revealed significant spatial and seasonal variations: the Inner Bay exhibited lower salinity (average 14 psu) from April to September due to freshwater inflows, while salinity increased (average 22 psu) from November to February. The Outer Bay, influenced by its connection to the South China Sea, maintained consistently high salinity levels (25–30 psu) year-round. Additionally, different models showed varying levels of effectiveness in Qinzhou Bay’s complex salinity environment; the IGWO–BPNN model, with its dynamic weight adjustment mechanism, demonstrated superior adaptability in areas with high salinity variability, outperforming other models. These findings suggest that the IGWO–BPNN model provides high accuracy and stability, supporting real-time, precise monitoring in Qinzhou Bay and similar coastal waters, thereby offering robust support for water quality management and marine conservation. Full article

This paper presents a novel algorithm named Five-element Cycle Integrated Mutation Optimization (FECOIMO) for solving the Traveling Thief Problem (TTP). The algorithm introduces a five-element cycle structure that integrates various mutation operations to enhance both global exploration and local exploitation capabilities. In experiments, FECOIMO was extensively tested on 39 TTP instances of varying scales and compared with five common metaheuristic algorithms: Enhanced Simulated Annealing (ESA), Improved Grey Wolf Optimization Algorithm (IGWO), Improved Whale Optimization Algorithm (IWOA), Genetic Algorithm (GA), and Profit-Guided Coordination Heuristic (PGCH). The experimental results demonstrate that FECOIMO outperforms the other algorithms across all instances, particularly excelling in large-scale instances. The results of the Friedman test show that FECOIMO significantly outperforms other algorithms in terms of average solution, maximum solution, and solution standard deviation. Additionally, although FECOIMO has a longer execution time, its complexity is comparable to that of other algorithms, and the additional computational overhead in solving complex optimization problems translates into better solutions. Therefore, FECOIMO has proven its effectiveness and robustness in handling complex combinatorial optimization problems. Full article

This work investigates the inverse coupled radiation–conduction problem for estimating thermophysical parameters and source terms by an improved grey wolf optimization (GWO). The transient coupled radiation–conduction heat transfer problem in participating slab media is solved by the finite volume method. The radiative intensities on both boundaries are adopted as known measurement information in the inverse model. To overcome the disadvantages of the original GWO algorithm, an improved grey wolf algorithm (IGWO) is developed by introducing the weight strategy and nonlinear factors. Three benchmark functions are adopted to prove that the IGWO has a faster convergence speed and higher estimation accuracy than the original one. The IGWO is applied to inverse the thermophysical parameters and source terms based on the coupled radiation–conduction model; the results indicate that the IGWO is accurate and effective for estimating refractive index, absorption coefficient, and source terms. Full article

As on-orbit space cameras evolve toward larger apertures, wider fields of view, and deeper cryogenic environments, achieving absolute radiometric calibration using an all-optical path blackbody reference source in orbit becomes increasingly challenging. Consequently, stars have emerged as a novel in-orbit standard source. However, due to differences in camera bands, directly obtaining the stellar radiance flux corresponding to specific camera bands is not feasible. In order to address this challenge, we propose a method for estimating radiance flux based on the MSX star catalog, which integrates a dual-band thermometry method with an improved grey wolf optimization (I-GWO) algorithm. In an experiment, we analyzed 351 stars with temperatures ranging from 4000 to 7000 K. The results indicate that our method achieved a temperature estimation accuracy of less than 10% for 83.5% of the stars, with an average estimation error of 5.82%. Compared with previous methods based on star catalogs, our approach significantly enhanced the estimation accuracy by 75.4%, improved algorithm stability by 91.3%, and reduced the computation time to only 3% of that required by other methods. Moreover, the on-orbit star calibration error using our stellar radiance flux estimation method remained within 5%. This study effectively leveraged the extensive data available in star catalogs, providing substantial support for the development of an infrared star calibration network, which holds significant value for the in-orbit calibration of large-aperture cameras. Future research will explore the potential applicability of this method across different spectral bands. Full article

With the continuous improvement in production efficiency and quality of life, the requirements of electrical equipment for power quality are also increasing. Accurate detection of various power quality disturbances is an effective measure to improve power quality. However, in practical applications, the dataset is often contaminated by noise, and when the dataset is not sufficient, the computational complexity is too high. Similarly, in the recognition process of artificial neural networks, the local optimum often occurs, which ultimately leads to low recognition accuracy for the trained model. Therefore, this article proposes a power quality disturbance classification strategy based on the variational mode decomposition (VMD) and improved support vector machine (SVM) algorithms. Firstly, the VMD algorithm is used for preprocessing disturbance denoising. Next, based on the analysis of typical fault characteristics, a multi-SVM model is used for disturbance classification identification. In order to improve the recognition accuracy, the improved Grey Wolf Optimization (IGWO) algorithm is used to optimize the penalty factor and kernel function parameters of the SVM model. The results of the final case study show that the classification accuracy of the proposed method can reach over 98%, and the recognition accuracy is higher than that of the other models. Full article

The path planning of unmanned aerial vehicles (UAVs) is crucial in UAV search and rescue operations to ensure efficient and safe search activities. However, most existing path planning algorithms are not suitable for post-disaster mountain rescue mission scenarios. Therefore, this paper proposes the IGWO-IAPF algorithm based on the fusion of the improved grey wolf optimizer (GWO) and the improved artificial potential field (APF) algorithm. This algorithm builds upon the grey wolf optimizer and introduces several improvements. Firstly, a nonlinear adjustment strategy for control parameters is proposed to balance the global and local search capabilities of the algorithm. Secondly, an optimized individual position update strategy is employed to coordinate the algorithm’s search ability and reduce the probability of falling into local optima. Additionally, a waypoint attraction force is incorporated into the traditional artificial potential field algorithm based on the force field to fulfill the requirements of three-dimensional path planning and further reduce the probability of falling into local optima. The IGWO is used to generate an initial path, where each point is assigned an attraction force, and then the IAPF is utilized for subsequent path planning. The simulation results demonstrate that the improved IGWO exhibits approximately a 60% improvement in convergence compared to the conventional GWO. Furthermore, the integrated IGWO-IAPF algorithm shows an approximately 10% improvement in path planning effectiveness compared to other traditional algorithms. It possesses characteristics such as shorter flight distance and higher safety, making it suitable for meeting the requirements of post-disaster rescue missions. Full article

The dynamic economic dispatch (DED) problem is a typical complex constrained optimization problem with non-smooth, nonlinear, and nonconvex characteristics, especially considering practical situations such as valve point effects and transmission losses, and its objective is to minimize the total fuel costs and total carbon emissions of generating units during the dispatch cycle while satisfying a series of equality and inequality constraints. For the challenging DED problem, a model of a dynamic economic dispatch problem considering fuel costs is first established, and then an improved grey wolf optimization algorithm (IGWO) is proposed, in which the exploitation and exploration capability of the original grey wolf optimization algorithm (GWO) is enhanced by initializing the population with a chaotic algorithm and introducing a nonlinear convergence factor to improve weights. Furthermore, a simple and effective constraint-handling method is proposed for the infeasible solutions. The performance of the IGWO is tested with eight benchmark functions selected and compared with other commonly used algorithms. Finally, the IGWO is utilized for three different scales of DED cases, and compared with existing methods in the literature. The results show that the proposed IGWO has a faster convergence rate and better global optimization capabilities, and effectively reduces the fuel costs of the units, thus proving the effectiveness of IGWO. Full article