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In complex indoor environments, traditional localization methods often suffer from non-line-of-sight (NLOS) and multipath problems, which lead to unsolvable or incorrectly solved mathematical localization models, thereby limiting localization accuracy. A localization method based on swarm intelligence optimization has been proposed to address this issue. The swarm intelligence optimization algorithm does not require solving matrix inversions and transforms the localization problem into a function optimization problem, which can obtain approximate optimal solutions. Nevertheless, optimization algorithms are beset with issues like sluggish convergence speed and proneness to getting trapped in local optima, thereby failing to satisfy the current practical requirements for localization. This paper proposes a new method that applies the grey wolf optimization (GWO) algorithm to ultra-wideband (UWB) indoor localization to enhance localization accuracy. It improves the GWO algorithm with four strategies. Firstly, a small-area optimization strategy near the target point is proposed. The Chan algorithm is adopted for initial tag localization, and the initial localization result is taken as a constraint to construct the search area of the GWO algorithm, thereby reducing the large space region to a small space region and enhancing optimization efficiency. Secondly, an improved Tent mapping, a nonlinear convergence factor, a fitness-weighted location update strategy, and an out-of-bounds reflection mechanism are designed to improve the GWO algorithm, referred to as the TIGWO algorithm. Finally, apply the TIGWO algorithm to determine the optimal location of the tag. The experimental results indicate that the proposed algorithm significantly enhances indoor localization accuracy. Compared to the Chan, Chan-Taylor, PSO, WOA, and GWO algorithms, the average localization accuracy has been enhanced by 59.65%, 63.41%, 40.97%, 45.97%, and 35.44%, respectively. In an equipment warehouse scenario, the X-axis, Y-axis, and Z-axis localization errors are 0.129 m, 0.101 m, and 0.154 m, respectively. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization, high-rise residential buildings has become a significant aspect of urban living. However, while high-rise residential buildings provide much housing, they also bring significant energy consumption. To raise the energy utilization efficiency of high-rise residential buildings, reduce energy consumption, and achieve sustainable development, this study focuses on high-rise residential buildings in cold regions. Through methods such as parametric modeling, joint simulation of building performance, multi-objective optimization algorithms, and improved grey wolf optimization algorithms, multi-objective optimization experiments are conducted to achieve optimal energy-saving effects. The outcomes denote that the average energy consumption of buildings remains at around 20.5 kW h/m2, and the maximum value of the last generation thermal comfort solution set is maintained at 62%, while the minimum value is maintained at 58%. The improved grey wolf optimization algorithm reduces training time, has better predictive ability, and can more accurately characterize changes in energy consumption of high-rise buildings. This study provides practical design methods and strategy references for high-rise residential buildings in the design phase by analyzing data, mining patterns, and summarizing design strategies. [ABSTRACT FROM AUTHOR]

Usually, the damage to structural members is not the same during earthquakes. As a result, the collapse times of members would be different from each other. So, one or a couple of stories would collapse sooner than others. Therefore, other members' potential and resisting ability would not be used completely. As a result, it needs to use all the potential of the structural members during the earthquake. There are several ways to quantify the damage value of buildings and structural elements. Many researchers use the Park–Ang index, one of the most common and accurate quantitative damage indices. This index can calculate the damage index for members, stories, and the whole structure separately by considering dissipated energy and displacement of members. Since control systems can cause a reduction in structural responses and, consequently, reduce damages by absorbing and dissipating energy, they have been used in many modern structures. The nonlinear viscous damper is one type of control system. This study aims to reduce the Park–Ang damage index of a 10-story steel moment frame modeled in OpenSees software and distribute the damage index uniformly in the structure's height. We will achieve this aim by optimizing nonlinear viscous dampers using a metaheuristic algorithm. This research chose six earthquakes that caused the most damage to the structure. The result shows that equipping the structure with an optimized damper has reduced the structural damage from all earthquakes. The reductions in structure damage index after the Gazli, Imperial Valley, Kocaeli, Loma Prieta, Northridge, and Duzce earthquakes are 82%, 68%, 62%, 49%, 53%, and 67%, respectively. Also, story damage indices are between 0 and 0.3 after controlling the structure. Practical Applications: During earthquakes, structural members' collapse times vary, leading to underutilization of other members' potential and resistance abilities due to differences in damage and collapse times. Damage to the first story of the structure exacerbates the situation, leading to the collapse of the entire structure. Therefore, it is crucial to fully utilize the potential of all structural members during an earthquake. The study aims to optimize the parameters of the nonlinear viscous dampers to reduce the Park–Ang damage index of a controlled steel moment frame. The dampers are designed to distribute the damage index uniformly throughout the structure's height, thereby reducing structural and story damage during earthquakes. Also, it helps to utilize the full potential of all structural members. The results show that equipping the structure with the optimized dampers reduces structural damage. [ABSTRACT FROM AUTHOR]

Multiple UAV three-dimensional path planning aims to provide reasonable and feasible flight paths for each UAV to its target while satisfying constraints and avoiding threats. Addressing the issues of slow search speed and poor path quality of current metaheuristic algorithms in solving multi-constraint and multi-threat three-dimensional spatial path planning, a path planning method based on a multi-strategy improved grey wolf optimization algorithm is proposed. Digital elevation models are used to complete three-dimensional spatial environment modeling, and a comprehensive evaluation function incorporating factors such as length, threat, height, and smoothness is established based on the flight scenario. By optimizing the initial population using a composite chaotic sequence and quasi-reverse learning strategy, and considering the crucial impact of upper-level wolves on population convergence in iterative stages, the algorithm’s ability to avoid local optima is enhanced using the elite wolf convex lens reflection learning strategy. Simulation experiments show that the multi-strategy improved grey wolf optimization algorithm improves the optimal, average, and worst values of the comprehensive evaluation function statistics by 6.1%, 5.1%, and 13.3%, respectively, compared to the original algorithm. This validates the effectiveness of the proposed method in solving multi-constraint and multi-threat three-dimensional spatial path planning problems.

Abstract The drilling rate index (DRI) of rocks is important for optimizing drilling operations, as it informs the choice of appropriate methods and equipment, ultimately improving the efficiency of rock excavation projects. This study presents a hybrid machine learning approach to predict the DRI of rocks accurately. By integrating grey wolf optimization with support vector machine (GWO-SVM), random forest (GWO-RF), and extreme gradient boosting (GWO-XGBoost) models, the aim was to enhance predictive accuracy. Among these, the GWO-XGBoost model exhibited superior predictive performance, achieving a coefficient of determination (R²) of 0.999, mean absolute error (MAE) of 0.00043, root mean square error (RMSE) of 1.98017, and severity index (SI) of 0.0350 during training. Testing results confirmed its accuracy with R² of 0.999, MAE of 0.00038, RMSE of 1.80790, and SI of 0.0312. Furthermore, the GWO-XGBoost model outperformed the other models in terms of precision, recall, f1-score, and multi-class confusion matrix results for each DRI class. The GWO-RF model also demonstrated high accuracy, ranking second, while the GWO-SVM model showed comparatively lower performance. This research aims to advance rock excavation practices by providing a highly accurate and reliable tool for DRI prediction. The results highlight the significant potential of the GWO-XGBoost model in improving DRI predictions, offering valuable intuitions and practical applications in the field.

The rotating machinery is composed of rolling bearing connection, so the fault identification of rolling bearing is a very critical task. We propose a bearing fault identification algorithm based on grey wolf optimizer (GWO) to address the common problems of high signal noise, inability of a single indicator to accurately reflect the true state of bearings, and optimization of support vector machine (SVM) prediction model parameters in bearing fault identification. First, the wavelet soft threshold is used to remove the noise of the original signal and then the empirical Fourier decomposition (EFD) is used in the decomposition and reconstruct signals. Second, in the aspect of feature extraction, the time and frequency domain features of the bearing data are selected to form the mixed domain features of the bearing signal. Finally, aiming at improving the bearing fault prediction accuracy, the GWO algorithm is used to optimize the parameters. Achievements: the signal‐to‐noise ratio can be effectively improved to 77.8 by using the wavelet denoising, and the parameter modeling optimized by the GWO algorithm can significantly improve the prediction accuracy, with an increase of about 3%–5%. It provides theoretical support for the optimization of bearing fault identification with this technology in the industrial field. [ABSTRACT FROM AUTHOR]

Measurement of mud gains in mud pits is a conventional method of detecting downhole gas kick and is still in use in present. Using this method, the detection of a gas kick sometimes remarkably lags the occurrence of the gas kick. Another gas kick detecting method is the monitoring-while-drilling method with which gas kick can be timely detected, but the functions of this method are very limited. In this study a new method with which a gas kick can be qualitatively detected in an early time and quantitatively explained is presented, the changes of the flowrates of the fluids in the annular space while a gas kick is encountered are analyzed, a gas kick risk index (KRI) is designed, and the mapping relationship between the KRI and the volume fraction of the kicked gas is derived. Based on the difference between downhole dual measurement points pressure (DDMP), using the grey wolf optimization (GWO) algorithm, a real-time method for calculating the flow velocities of the fluids in the annular space is constructed. Using a simulated gas kick scenario, the stability and effectiveness of the method for early detection of gas kick are analyzed. The study shows that when a gas kick occurs, the flowrates of the fluids in the annular space are increasing, and this can be used as a key characteristic parameter for gas kick detection. The volume fraction of the gas in the annular space has a linear relationship with KRI. Errors made in calculating the flowrate of the fluids in the annular space first decrease and then increase as the distance between the two measurement points increases, and are less affected by the errors made in pressure and temperature measurement. Using this new method, the lag time for detecting a gas kick is 13.8 min, and the inversion error of the gas volume fraction in the annular space is less than 10%. This method is not only able to detect gas kick earlier, it also provides key parameters for well control design such as the gas fraction of the fluids in the annular space at a mud gain in the mud pits of only 0.017 m3, a volume that does not cause the fluid levels in the mud pits to change significantly. [ABSTRACT FROM AUTHOR]

The inerter-based dynamic vibration absorber (DVA) is a promising technique for vibration control. In most studies, the inerter is usually mounted in the same direction as that of the motion, which may not adequately reflect the vibration suppression capability and the two-terminal inertial feature of the inerter, and even weakens the performance of vibration absorbers. Considering the potentiality of improving the control performance by unconventional mounting methods, a rarely studied structure of geometrically nonlinear inerters is introduced into the vibration absorber system. This novel vibration absorber is presented to investigate the following issues, that is, the unknown coupled dynamical behavior between the complex nonlinear force with inertia, damping, and stiffness terms generated by this structure and the vibration absorber system, the possibility of vibration absorption facilitated by this structure, and the full utilization of the two-terminal inertial feature of the inerter. The approximate solutions of the system are obtained using the harmonic balance method. The influence of each variable on the system response is analyzed, and the system parameters are optimized by using the grey wolf algorithm. In addition to the ordinary resonance phenomenon, there are also dynamic features such as soft characteristic jump behavior and response loops at certain parameter range. As a nonlinear system, this model is more stable than the nonlinear energy sink (NES). The optimized amplitude-frequency curves are equal-peak stable, similar to the linear vibration absorbers. The robustness of system parameters is high for small inerter-mass ratios or excitation amplitudes, which is better than DVAs. Compared to the classical linear vibration absorber, NES, and improved NES, the vibration suppression capacity and damping bandwidth of this model are enhanced. In comparison, it is also found that this model has smaller optimum parameters than the classical NES and equivalent inerter-enhanced DVA and NES. This model offers a new solution for the design and implementation of passive vibration absorbers with comprehensive performance. [ABSTRACT FROM AUTHOR]

In this article, the Grey Wolf Optimization (GWO) algorithm is discussed, which is considered a global optimization technique capable of improving the global search of particles across the entire search space. The Grey Wolf Algorithm is a relatively new algorithm inspired by the hunting behavior of grey wolves and was first introduced by Mirjalili and his colleagues in 2014. This algorithm has been applied in a few cases to geophysical data. The main goal of the Grey Wolf Optimization Algorithm is to optimize objective functions by drawing inspiration from the behavior of wolf packs to reach better and optimal solutions. Therefore, each of the wolves represents a model with dimensions corresponding to the number of model parameters. The parameters of each wolf (model) include amplitude Coefficient (A), Depth (z), Shape Factor (q), and Center of Mass (x0). The designed algorithm is run for 300 iterations with 30 search agents (wolves), and it is tested on the objective function 10 times, taking the average optimal solution provided by the software as the final parameter. To evaluate the performance of this method, the gravity field of three synthetic models, namely a sphere, a horizontal cylinder, and a vertical cylinder, both with and without the addition of random noise, is analyzed. Frequency domain estimation of the model parameters is used for each of these models. The results show that the proposed algorithm can accurately estimate the model parameters. Subsequently, the Grey Wolf Optimization Algorithm is applied to analyze the gravity field of the Humble salt dome area in the United States. The results for the studied region indicate that the buried object's center of mass is approximately 4.76 kilometers deep, the domain coefficient is 294.25 units, and its approximate shape is calculated to be similar to a sphere with a calculated shape factor of 1.47, which aligns well with previous studies. The advantage of GWO inversion is its ability to fine-tune the parameters quickly, avoid local minima, and estimate the optimal parameter values. In this study, the Root Mean Square (RMS) statistical measure is used to compare the measured gravity field and the gravity field calculated based on the estimated parameters. The error between the gravity field values of the synthetic models and the values calculated from the optimal parameters obtained by the Grey Wolf Optimization Algorithm is very small, indicating the algorithm's good performance. Furthermore, the sensitivity of this algorithm to various levels of random noise is investigated, and the results indicate the algorithm's stability against random noise. [ABSTRACT FROM AUTHOR]

The Arithmetic Optimization Algorithm (AOA) is a novel metaheuristic inspired by mathematical arithmetic operators. Due to its simple structure and flexible parameter adjustment, the AOA has been applied to solve various engineering problems. However, the AOA still faces challenges such as poor exploitation ability and a tendency to fall into local optima, especially in complex, high-dimensional problems. In this paper, we propose a Hybrid Improved Arithmetic Optimization Algorithm (HIAOA) to address the issues of susceptibility to local optima in AOAs. First, grey wolf optimization is incorporated into the AOAs, where the group hunting behavior of GWO allows multiple individuals to perform local searches at the same time, enabling the solution to be more finely tuned and avoiding over-concentration in a particular region, which can improve the exploitation capability of the AOA. Second, at the end of each AOA run, the follower mechanism and the Cauchy mutation operation of the Sparrow Search Algorithm are selected with the same probability and perturbed to enhance the ability of the AOA to escape from the local optimum. The overall performance of the improved algorithm is assessed by selecting 23 benchmark functions and using the Wilcoxon rank-sum test. The results of the HIAOA are compared with other intelligent optimization algorithms. Furthermore, the HIAOA can also solve three engineering design problems successfully, demonstrating its competitiveness. According to the experimental results, the HIAOA has better test results than the comparator. [ABSTRACT FROM AUTHOR]

The drilling rate index (DRI) of rocks is important for optimizing drilling operations, as it informs the choice of appropriate methods and equipment, ultimately improving the efficiency of rock excavation projects. This study presents a hybrid machine learning approach to predict the DRI of rocks accurately. By integrating grey wolf optimization with support vector machine (GWO-SVM), random forest (GWO-RF), and extreme gradient boosting (GWO-XGBoost) models, the aim was to enhance predictive accuracy. Among these, the GWO-XGBoost model exhibited superior predictive performance, achieving a coefficient of determination (R²) of 0.999, mean absolute error (MAE) of 0.00043, root mean square error (RMSE) of 1.98017, and severity index (SI) of 0.0350 during training. Testing results confirmed its accuracy with R² of 0.999, MAE of 0.00038, RMSE of 1.80790, and SI of 0.0312. Furthermore, the GWO-XGBoost model outperformed the other models in terms of precision, recall, f1-score, and multi-class confusion matrix results for each DRI class. The GWO-RF model also demonstrated high accuracy, ranking second, while the GWO-SVM model showed comparatively lower performance. This research aims to advance rock excavation practices by providing a highly accurate and reliable tool for DRI prediction. The results highlight the significant potential of the GWO-XGBoost model in improving DRI predictions, offering valuable intuitions and practical applications in the field. [ABSTRACT FROM AUTHOR]

In order to achieve the best crushing effect of the 6-DOF robotic crusher, a multi-objective optimal control method for the 6-DOF robotic crusher has been proposed. Taking the mass fraction of crushed products below 12 mm, total energy consumption, effective energy consumption, output, and wear as the working indexes, and taking the suspension point, precession angle, and swing frequency of the mantle as the working conditions of the crusher, the working indexes under different working conditions are calculated. And, based on the above parameters, the optimization objective function of the 6-DOF robotic crusher is obtained. The weight determination method of fuzzy multiple attributes decision making (FMADM) is used to determine the equivalent wear and the optimization target weight. Compared with the original scheme, the output increases and the energy consumption decreases significantly. The results can be used as a reference for the control strategy of the 6-DOF robotic crusher. It can also be used as a reference for the design of a traditional cone crusher. [ABSTRACT FROM AUTHOR]

Taking the planar mobile stereo garage as the research object, in order to improve its overall access efficiency, combined with the operation characteristics of the garage, this paper studies the access strategy and Automated Guided Vehicle (AGV) path planning. Firstly, considering the actual access process of the garage and the different access conditions in the flat peak, storage peak and pickup peak periods, the mathematical model of access strategy is established, and the access strategy suitable for the garage is selected through calculation and analysis. Secondly, based on the traditional grey wolf optimization (GWO) algorithm, an improved GWO algorithm based on initial population opposition-based learning and two-stage nonlinear convergence factor is proposed and applied to the static obstacle avoidance path planning problem of AGV in the stereo garage. Compared with the genetic algorithm and the traditional GWO algorithm, the results show that the improved GWO algorithm has outstanding performance in terms of convergence speed, path length. [ABSTRACT FROM AUTHOR]

The Grey Wolf Optimization (GWO) is a highly effective meta-heuristic algorithm leveraging swarm intelligence to tackle real-world optimization problems. However, when confronted with large-scale problems, GWO encounters hurdles in convergence speed and problem-solving capabilities. To address this, we propose an Improved Adaptive Grey Wolf Optimization (IAGWO), which significantly enhances exploration of the search space through refined search mechanisms and adaptive strategy. Primarily, we introduce the incorporation of velocity and the Inverse Multiquadratic Function (IMF) into the search mechanism. This integration not only accelerates convergence speed but also maintains accuracy. Secondly, we implement an adaptive strategy for population updates, enhancing the algorithm's search and optimization capabilities dynamically. The efficacy of our proposed IAGWO is demonstrated through comparative experiments conducted on benchmark test sets, including CEC 2017, CEC 2020, CEC 2022, and CEC 2013 large-scale global optimization suites. At CEC2017, CEC 2020 (10/20 dimensions), CEC 2022 (10/20 dimensions), and CEC 2013, respectively, it outperformed other comparative algorithms by 88.2%, 91.5%, 85.4%, 96.2%, 97.4%, and 97.2%. Results affirm that our algorithm surpasses state-of-the-art approaches in addressing large-scale problems. Moreover, we showcase the broad application potential of the algorithm by successfully solving 19 real-world engineering challenges. [ABSTRACT FROM AUTHOR]

The Grey Wolf Optimization (GWO) algorithm, a computational optimization method inspired by the social behavior of wolves, has recently been effectively used to solve optimization and routing problems. This paper proposes a metaheuristic approach named Grey Wolf Optimization (GWO) inspired by grey wolves. Four types of grey wolves, namely alpha, beta, delta, and omega, are employed to simulate the leadership hierarchy. Additionally, three main stages of hunting—searching for prey, encircling prey, and attacking prey—are implemented. Overall, this paper examines how the combination of the chessboard method and the Grey Wolf Optimization algorithm can optimize the path planning of a mobile robot in both static and dynamic environments. The objective of this research is to shorten the path, minimize the final position to the target, avoid collisions, and prevent local minima. This paper investigates the Grey Wolf Optimization algorithm as an effective method for solving the routing problem. Simulation results demonstrate that using this algorithm leads to significant improvements in the robot's efficiency and enhanced path-planning performance in complex and dynamic environments

Abstract This paper proposes a transformer fault diagnosis method based on ACGAN and CGWO-LSSVM to address the problem of misjudgment and low diagnostic accuracy caused by the small number and uneven distribution of some fault samples in transformer fault diagnosis. Firstly, generate adversarial networks through auxiliary classification conditions, The ACGAN method expands a small and imbalanced number of samples to obtain balanced and expanded data; Secondly, the non coding ratio method is used to construct the characteristics of dissolved gases in oil, and kernel principal component analysis is used, KPCA method for feature fusion; Finally, using the improved cubic gray wolf optimization algorithm, CGWO for least square support vector machines, optimize the parameters of the LSSVM model and construct a transformer fault diagnosis model. The results show that the proposed method has a low false alarm rate and a diagnostic accuracy of 97.66%, compared to IGOA-LSSVM the IChOA-LSSVM and PSO-LSSVM methods improved accuracy by 0.12, 1.76, and 2.58%, respectively. This method has been proven to solve the problems of misjudgment and low diagnostic accuracy caused by small sample sizes and uneven distribution. It is suitable for multi classification fault diagnosis of transformer imbalanced datasets and is superior to other methods.

A smart city (SC) includes different systems that are highly interconnected. Transportation and energy systems are two of the most important ones that must be operated and planned in a coordinated framework. In this paper, with the complete implementation of the SC, the performance of each of the network elements has been fully analyzed; hence, a nonlinear model has been presented to solve the operation and planning of the SC model. In the literature, water treatment issues, as well as energy hubs, subway systems (SWSs), and transportation systems have been investigated independently and separately. A new method of subway and electric vehicle (EV) interaction has resulted from stored energy obtained from subway braking and EV parking. Hence, considering an SC that simultaneously includes renewable energy, transportation systems such as the subway and EVs, as well as the energy required for water purification and energy hubs, is a new and unsolved challenge. In order to solve the problem, in this paper, by presenting a new system of the SC, the necessary planning to minimize the cost of the system is presented. This model includes an SWS along with plug-in EVs (PEVs) and different distributed energy resources (DERs) such as Photovoltaics (PVs), Heat Pumps (HPs), and stationary batteries. An improved grey wolf optimizer has been utilized to solve the nonlinear optimization problem. Moreover, four scenarios have been evaluated to assess the impact of the interconnection between SWSs and PEVs and the presence of DER technologies in the system. Finally, results were obtained and analyzed to determine the benefits of the proposed model and the solution algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes a transformer fault diagnosis method based on ACGAN and CGWO-LSSVM to address the problem of misjudgment and low diagnostic accuracy caused by the small number and uneven distribution of some fault samples in transformer fault diagnosis. Firstly, generate adversarial networks through auxiliary classification conditions, The ACGAN method expands a small and imbalanced number of samples to obtain balanced and expanded data; Secondly, the non coding ratio method is used to construct the characteristics of dissolved gases in oil, and kernel principal component analysis is used, KPCA method for feature fusion; Finally, using the improved cubic gray wolf optimization algorithm, CGWO for least square support vector machines, optimize the parameters of the LSSVM model and construct a transformer fault diagnosis model. The results show that the proposed method has a low false alarm rate and a diagnostic accuracy of 97.66%, compared to IGOA-LSSVM the IChOA-LSSVM and PSO-LSSVM methods improved accuracy by 0.12, 1.76, and 2.58%, respectively. This method has been proven to solve the problems of misjudgment and low diagnostic accuracy caused by small sample sizes and uneven distribution. It is suitable for multi classification fault diagnosis of transformer imbalanced datasets and is superior to other methods. [ABSTRACT FROM AUTHOR]

In mechanical equipment, if bearing components fail, it can cause serious equipment damage and even threaten human life safety. Therefore, equipment bearings fault diagnosis is of great meaning. In the study of bearing fault diagnosis, an improved gray wolf optimization algorithm is put forward to optimize the support vector machine model. The model improves the convergence factor of the algorithm, and then optimizes the penalty factor and KF parameters of the support vector machine to enhance the accuracy of fault classification. At the same time, in the problem of fault identification, the introduction of adaptive noise set empirical mode decomposition and the combination of permutation entropy are studied to minimize the impact of noise on the identification model. The experimental outcomes indicated that the algorithm proposed in the study had an average fitness value and a standard deviation fitness value of 0 in the benchmark test function and 94.55% accuracy in overall fault identification. The permutation entropy of the vibration signal in the normal state of the bearing was within the range of [0.13, 0.52], which has a more stable state compared to the fault state. The results show that the improved grey Wolf optimization algorithm is applied to the optimization of support vector machine, combined with the adaptive noise set empirical mode decomposition and permutation entropy, and the identification and classification results of bearing faults are successfully improved, making the proposed method feasible in bearing fault diagnosis, and providing a more effective scheme for fault diagnosis. [ABSTRACT FROM AUTHOR]

The Grey Wolf Optimization Algorithm (GWO) is a member of the swarm intelligence algorithm family, which possesses the highlights of easy realization, simple parameter settings and wide applicability. However, in some large-scale application problems, the grey wolf optimization algorithm easily gets trapped in local optima, exhibits poor global exploration ability and suffers from premature convergence. Since grey wolf's update is guided only by the best three wolves, it leads to low population multiplicity and poor global exploration capacity. In response to the above issues, we design a multi-strategy collaborative grey wolf optimization algorithm (NOGWO). Firstly, we use a random walk strategy to extend the exploration scope and enhance the algorithm's global exploration capacity. Secondly, we add an opposition-based learning model influenced by refraction principle to generate an opposite solution for each population, thereby improving population multiplicity and preventing the algorithm from being attracted to local optima. Finally, to balance local exploration and global exploration and elevate the convergence effect, we introduce a novel convergent factor. We conduct experimental testing on NOGWO by using 30 CEC2017 test functions. The experimental outcomes indicate that compared with GWO and some swarm intelligence algorithms, NOGWO has better global exploration capacity and convergence accuracy. In addition, we also apply NOGWO to three engineering problems and an unmanned aerial vehicle path planning problem. The outcomes of the experiment suggest that NOGWO performs well in solving these practical problems. [ABSTRACT FROM AUTHOR]

Blast furnace ironmaking plays an important role in modern industry and the development of the economy. A reasonable ingredient scheme is crucial for energy efficiency and emission reduction in blast furnace production. Determining the right blast furnace ingredients is a complicated process; therefore, this study examines the optimization of the ingredient ratio. In this paper a model of the blast furnace ingredients is established by considering cost of per ton iron, CO2 emissions, and the theoretical coke ratio as the objective functions; ingredient parameters, process parameters, main and by-product parameters as variables; and the blast furnace smelting theory and equilibrium equation as constraints. Then, the model is solved by using an improved grey wolf optimization algorithm and an improved multi-objective grey wolf optimization algorithm. Using the data collected from the steel mill, the conclusion is that multi-objective optimization can consider the indexes of each target, so that the values of all the targets are excellent; we also compared the multi-objective solution results with the original production scheme of the steel mill, and we found that using the blast furnace ingredient scheme optimized in this study can reduce the cost of iron per ton, CO2 emissions per ton, and the theoretical coke ratio in blast furnace production by 350 CNY/t, 1000 kg/t, and 20 kg/t, respectively, compared with the original production plan. Thus, steel mill decision makers can choose the blast furnace ingredients according to different business strategies and the actual needs of steel mills can be better met. [ABSTRACT FROM AUTHOR]

With recent advances in wide-area measurement systems (WAMS), wide-area damping controllers (WADCs) based on remote signals can effectively damp inter-area oscillations. However, these signals are inherently subjected to communication time-delay which may adversely affect the damping of inter area oscillations. The purpose of this paper is to present a optimization-based method for designing a WADC for a static synchronous series compensator (SSSC) to improve the damping of inter-area oscillations by considering the time delays of remote control signals. For this aim, the control gain value of the WADC, shifting of the critical modes to a desirable area, and the maximum time delay margins are simultaneously considered as an objective function. Moreover, to design a WADC as minimum-phase structure, the suitable constraints have been determined and added to the objective function as penalty factors. A grey wolf optimization (GWO) algorithm is utilized to solve the optimization problem. Robustness of the designed SSSC-based WADC in amplitude and phase control channels against the time delay has been compared and then the best control channels to damp inter-area oscillations has been selected. Based on simulations and statistical results conducted on 4 and 50 machine multi-machine power systems, it is evident that the proposed method based on the GWO outperforms other techniques in achieving the optimal design of a SSSC-based WADC for damping inter-area oscillations.

Copyright of Journal of Henan University of Science & Technology, Natural Science is the property of Editorial Office of Journal of Henan University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ensuring the security of germplasm resources is of great significance for the sustainable development of agriculture and ecological balance. By combining the morphological characteristics of maize seeds with hyperspectral data, maize variety classification has been achieved using machine learning algorithms. Initially, the morphological data of seeds are obtained from images, followed by the selection of feature subsets using Recursive Feature Elimination (RFE) and Select From Model (SFM) methods, indicating that features selected by RFE exhibit better performance in maize seed classification. For hyperspectral data (350–2500 nm), Competitive Adaptive Re-weighted Sampling (CARS) and the Successive Projections Algorithm (SPA) are employed to extract feature wavelengths, with the SPA algorithm demonstrating superior performance in maize seed classification tasks. Subsequently, the two sets of data are merged, and a Random Forest (RF) classifier optimized by Grey Wolf Optimization (GWO) is utilized. Given the limitations of GWO, strategies such as logistic chaotic mapping for population initialization, random perturbation, and final replacement mechanisms are incorporated to enhance the algorithm's search capabilities. The experimental results show that the proposed ZGWO-RF model achieves an accuracy of 95.9%, precision of 96.2%, and recall of 96.1% on the test set, outperforming the unimproved model. The constructed model exhibits improved identification effects on multi-source data, providing a new tool for non-destructive testing and the accurate classification of seeds in the future. [ABSTRACT FROM AUTHOR]

The DC voltage ratio standard device is an important tool for calibrating DC voltage transformers. At the 1100 kV voltage level, an increase in electric field intensity will increase the local heat generated inside the device, affecting the accuracy of its measurement. Using a suitable grading ring can even out the electric field intensity and reduce the maximum field strength to improve its measurement accuracy. This article mainly optimizes the design of the grading-ring structure of the 1100 kV DC voltage ratio standard device. First, a finite-element model of the 1100 kV DC voltage ratio standard device was built based on ANSYS; the electric field distribution around the voltage divider was calculated and analyzed, and a data set was constructed based on the calculation results. Secondly, for the optimization of electric field strength, this article presents the design of the nation standard device neural network, which learns the relationship between the structural parameters of the toroidal core and the field strength under the PyTorch 1.8 deep learning framework. Due to the strong convergence performance, few parameters, and ease of implementation of the grey wolf optimization algorithm, this study selected this algorithm to optimize the structural parameters of the grading ring. Finally, simulation examples are established in Python for validation. The experimental results indicate that the maximum field strength of the grading ring decreased from 12,161.1348 V/cm to 10,009.2881 V/cm, a reduction of 17.69%. The optimized structural parameters of the grading ring effectively reduced the electric field intensity around the 1100 kV DC voltage proportional standard device, providing a reliable and practical design approach for the selection of the DC voltage ratio standard device. [ABSTRACT FROM AUTHOR]

Aiming to address power consumption issues of various equipment in metro stations and the inefficiency of peak shaving and valley filling in the power supply system, this study presents an economic optimization scheduling method for the multi-modal “source-network-load-storage” system in metro stations. The proposed method, called the Improved Gray Wolf Optimization Algorithm (IGWO), utilizes objective evaluation criteria to achieve economic optimization. First, construct a mathematical model of the “sourcenetwork- load-storage” joint system with the metro station at its core. This model should consider the electricity consumption within the station. Secondly, a two-layer optimal scheduling model is established, with the upper model aiming to optimize peak elimination and valley filling, and the lower model aiming to minimize electricity consumption costs within a scheduling cycle. Finally, this paper introduces the IGWO optimization approach, which utilizes meta-models and the Improved Gray Wolf Optimization Algorithm to address the nonlinearity and computational complexity of the two-layer model. The analysis shows that the proposed model and algorithm can improve the solution speed and minimize the cost of electricity used by about 5.5% to 8.7% on the one hand, and on the other hand, it improves the solution accuracy, and at the same time effectively realizes the peak shaving and valley filling, which provides a proof of the effectiveness and feasibility of the new method.

For power transformers, the gas content in oil is used as the fault input feature quantity, and the accuracy of diagnosis results is not satisfactory. The problem of low accuracy of optimized extreme learning machine (ELM) of grey wolf optimization (GWO) algorithm is proposed, and a hybrid intelligent fault diagnosis method based on random forest and improved optimized extreme learning machine of grey wolf optimization algorithm is proposed. Firstly, the importance of the candidate gas ratios is score by random forest and reassembled into five groups of feature parameters in order of importance from highest to lowest and used as the input feature quantity of the model. Secondly, the extreme learning machine is optimized to randomly generate weights and thresholds using the improved grey wolf optimization algorithm to improve the prediction accuracy of the model. Finally, the simulation experiments and comparative test analysis show that the fault diagnosis model has particular effectiveness in transformer fault diagnosis.

The rapid growth of data quantity directly leads to the increasing feature dimension, which challenges machine learning and data mining. Wrapper-based intelligent swarm algorithms are effective solution techniques. The Grey Wolf Optimization (GWO) algorithm is a novel intelligent population algorithm. Simple principles and few parameters characterize it. However, the basic GWO has disadvantages, such as difficulty coordinating exploration and exploitation capabilities and premature convergence. As a result, GWO fails to identify many irrelevant and redundant features. To improve the performance of the basic GWO algorithm, this paper proposes a velocity-guided grey wolf optimization algorithm with adaptive weights and Laplace operators (VGWO-AWLO). Firstly, by introducing a uniformly distributed dynamic adaptive weighting mechanism, the control parameters $a$ are guided to undergo nonlinear dynamic changes to achieve a good transition from the exploratory phase to the development phase. Second, a velocity-based position update formula is designed with an individual memory function to enhance the local search capability of individual grey wolves and drive them to converge to the optimal solution. Thirdly, a Laplace cross-operator strategy is applied to increase the population diversity and help the grey wolf population escape from the local optimal solution. Finally, the VGWO-AWLO algorithm is evaluated for its comprehensive performance in terms of classification accuracy, dimensionality approximation, convergence, and stability in 18 classified datasets. The experimental results show that the classification accuracy and convergence speed of VGWO-AWLO are better than the basic GWO, GWO variants, and other state-of-the-art meta-heuristic algorithms.

Classification algorithms are one of the important research topics in the artificial intelligence, widely applied in various scientific and engineering fields. Extreme learning machine is a single hidden layer feed-forward neural network algorithm. Compared with traditional neural network models, the training speed and the generalization ability are also better. In terms of methodology, this study first innovatively improves the traditional Grey Wolf Optimization (GWO) algorithm to enhance its convergence and search ability. Specific improvement measures include implementing the reverse learning strategy to reduce the initial dependence of the algorithm on population distribution, and adding exploration perception strategy to enhance its global search ability by calculating heuristic factors, so as to identify the global optimal solution more effectively. The results showed that the improved W-DH-ELM model had excellent performance on multiple standard data sets. In particular, the average accuracy was more than 90%, which was significantly higher than other benchmark classification models. In terms of operation efficiency, the running time of the new model on different data sets was significantly reduced, accounting for less than 25%, and the lowest running time was only 4.89%. These experimental results verify the effectiveness of the introduced intelligent optimization algorithm in improving the performance of traditional ELM model without changing the original model structure. The improved W-DH-ELM model not only maintains the fast training performance of ELM, but also has higher accuracy and stability, which shows its superiority in dealing with complex classification tasks. In summary, the weighted two-hidden layer extreme learning machine optimized by the improved GWO proposed in this study has significant advantages in classification problems, providing a new perspective for future machine learning applications and research.

In order to achieve the best crushing effect of the 6-DOF robotic crusher, a multi-objective optimal control method for the 6-DOF robotic crusher has been proposed. Taking the mass fraction of crushed products below 12 mm, total energy consumption, effective energy consumption, output, and wear as the working indexes, and taking the suspension point, precession angle, and swing frequency of the mantle as the working conditions of the crusher, the working indexes under different working conditions are calculated. And, based on the above parameters, the optimization objective function of the 6-DOF robotic crusher is obtained. The weight determination method of fuzzy multiple attributes decision making (FMADM) is used to determine the equivalent wear and the optimization target weight. Compared with the original scheme, the output increases and the energy consumption decreases significantly. The results can be used as a reference for the control strategy of the 6-DOF robotic crusher. It can also be used as a reference for the design of a traditional cone crusher.

In order to identify Panax notoginseng powders from different root parts, an electronic nose and gas chromatography-mass spectrometry (GC-MS) were used to analyze the volatile components of the whole root powder, rhizome powder, taproot powder, lateral root powder and fibrous root powder of P. notoginseng. The data obtained were analyzed by multiple comparison. The statistical learning method was used to extract eight time-domain features from the response curves of the electronic nose, and correlation analysis was carried out. Three feature selection algorithms were used to reduce the dimension of the feature data. Classification models were built using support vector machine (SVM), least square support vector machine (LSSVM) or extreme learning machine (ELM) based on the original feature data or the three kinds of feature selection data. The grey wolf optimization (GWO) algorithm was introduced to optimize the parameters gam and sig2 in the classification model. The results showed that a total of 31 volatile compounds were detected in the five P. notoginseng powders. The best GWO-IRIV-LSSVM model could effectively distinguish the electronic nose data, with 97.5% accuracy for the test set. Moreover, the volatile composition of the five samples differed mainly in terms of the contents of total volatiles, alkanes, and aromatic compounds, which was consistent with the results of GC-MS. The method used in this study can be used for the detection of high-quality P. notoginseng powder from geo-authentic production areas mixed with low-quality P. notoginseng powder.