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Use an adaptive network fuzzy inference system model for estimating the compressive strength of high-performance concrete with two optimizers improved grey wolf algorithm and Dragonfly optimization algorithm

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2023年第2期6卷 263-276页

作者： Hu, Xiaoxuan Zhejiang Guangsha Vocat & Tech Univ Construct Sch Civil Engn Architecture Dongyang 322100 Peoples R China

In today's world, high-performance concrete (HPC) has solved many construction problems. HPC is an advanced concrete used to construct dams, silos, skyscrapers, etc. The most important difference between this concrete and ordinary concrete is its compressive strength (CS). The compressive strength of HPC is a result of the additives used in its construction. Fly ash and blast furnace slag are admixtures that increase the compressive strength of concrete. Adding these materials can reduce the amount of cement, bringing economic efficiency. Furthermore, compressive strength can be obtained in the laboratory and by software. The laboratory model is not economical and does not save time, so the software model is replaced. The use of artificial intelligence and adaptive network fuzzy inference system (ANFIS) modeling can be a suitable solution for estimating the compressive strength of high-performance concrete. To get satisfactory results, the model can be combined with optimizers. improved Gray wolf algorithm (I_GWO) and Dragonfly optimization algorithm (DA) are innovative algorithms that estimate estimated compressive strength for lab results. In modeling, certain evaluators determine the superiority of the combined model. This article notes that the ANFISDA hybrid model performs better than ANFISI_GWO.

关键词： High-performance concrete Compressive Strength improved grey wolf algorithm Dragonfly optimization algorithm Adaptive network fuzzy inference system

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Economical-technical-environmental operation of power networks with wind-solar-hydropower generation using analytic hierarchy process and improved grey wolf algorithm

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AIN SHAMS ENGINEERING JOURNAL 2021年第3期12卷 2717-2734页

作者： Rawa, Muhyaddin Abusorrah, Abdullah Bassi, Hussain Mekhilef, Saad Ali, Ziad M. Aleem, Shady H. E. Abdel Hasanien, Hany M. Omar, Ahmed I. King Abdulaziz Univ Ctr Res Excellence Renewable Energy & Power Syst Jeddah 21589 Saudi Arabia King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia King Abdulaziz Univ Fac Engn Dept Elect Engn Rabigh 25732 Saudi Arabia Univ Malaya Dept Elect Engn Power Elect & Renewable Energy Res Lab Kuala Lumpur 50603 Malaysia Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawaser Elect Engn Dept Al Kharj 11991 Saudi Arabia Aswan Univ Aswan Fac Engn Elect Engn Dept Aswan 81542 Egypt Arab Acad Sci Coll Engn & Technol Elect Energy Dept Technol & Maritime Transport Giza 12577 Egypt ETA Elect Co Power Qual Solut Giza 12111 Egypt Future Univ Egypt Fac Engn & Technol Cairo 11835 Egypt Higher Inst Engn El Shorouk City Elect Power & Machines Engn Cairo Egypt

This paper presents an economical-technical-environmental dispatch (ETED) model for an adapted IEEE 30-bus system incorporated with thermal and a mix of renewable energy sources (RESs). Total fuel costs, active power losses, and emissions level minimization is the main aim. Different equality and inequality limits involving prohibited operating zones (POZs) are considered as system restrictions. Metaheuristic optimization techniques - moth-flame optimization, salp swarm algorithm, improved grey wolf optimizer, and multi-verse optimizer - are employed to find the best solution for the generation cost, losses, and emissions. Various scenarios are examined to approve the ability of the formulated optimization model in solving the problem. A weighted sum strategy using the analytic hierarchy process (AHP) is used to convert the multi-objective problem into a normalized single-objective one. The AHP-ETED model presented in this work can significantly minimize fuel costs to 902.4951 $/h, lower emission levels as 0.09785 t/h, and achieve a lower power loss of 2.4110 MW. The results attained validate that the IGWO outperforms the other considered algorithms in finding the best solution to the ETED problem. (c) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Analytic hierarchy process Energy Economical-technical-environmental dispatch improved grey wolf algorithm Optimal power flow Power systems Renewable energy sources

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Bearing Fault-Detection Method Based on improved grey wolf algorithm to Optimize Parameters of Multistable Stochastic Resonance

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SENSORS 2023年第14期23卷 6529页

作者： Huang, Weichao Zhang, Ganggang Xian Univ Technol Shannxi Key Lab Complex Syst Control & Intelligent Xian 710048 Peoples R China Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Peoples R China

In an effort to overcome the problem that the traditional stochastic resonance system cannot adjust the structural parameters adaptively in bearing fault-signal detection, this article proposes an adaptive-parameter bearing fault-detection method. First of all, the four strategies of Sobol sequence initialization, exponential convergence factor, adaptive position update, and Cauchy-Gaussian hybrid variation are used to improve the basic grey wolf optimization algorithm, which effectively improves the optimization performance of the algorithm. Then, based on the multistable stochastic resonance model, the structure parameters of the multistable stochastic resonance are optimized through improving the grey wolf algorithm, so as to enhance the fault signal and realize the effective detection of the bearing fault signal. Finally, the proposed bearing fault-detection method is used to analyze and diagnose two open-source bearing data sets, and comparative experiments are conducted with the optimization results of other improved algorithms. Meanwhile, the method proposed in this paper is used to diagnose the fault of the bearing in the lifting device of a single-crystal furnace. The experimental results show that the fault frequency of the inner ring of the first bearing data set diagnosed using the proposed method was 158 Hz, and the fault frequency of the outer ring of the second bearing data set diagnosed using the proposed method was 162 Hz. The fault-diagnosis results of the two bearings were equal to the results derived from the theory. Compared with the optimization results of other improved algorithms, the proposed method has a faster convergence speed and a higher output signal-to-noise ratio. At the same time, the fault frequency of the bearing of the lifting device of the single-crystal furnace was effectively diagnosed as 35 Hz, and the bearing fault signal was effectively detected.

关键词： multistable stochastic resonance adaptive parameter improved grey wolf algorithm bearing fault detection

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Epileptic EEG Signal Detection Using Variational Modal Decomposition and improved grey wolf algorithm

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SENSORS 2023年第19期23卷 8078-8078页

作者： Sun, Yongxin Chen, Xiaojuan Changchun Univ Sci & Technol Coll Elect Informat Engn Changchun 130022 Peoples R China Baicheng Normal Univ Coll Phys & Elect Informat Baicheng 137099 Peoples R China

Epilepsy does great harm to the human body, and even threatens human life when it is serious. Therefore, research focused on the diagnosis and treatment of epilepsy holds paramount clinical significance. In this paper, we utilized variational modal decomposition (VMD) and an enhanced grey wolf algorithm to detect epileptic electroencephalogram (EEG) signals. Data were extracted from each patient's preseizure period and seizure period of 200 s each, with every 2 s as a segment, meaning 100 data points could be obtained for each patient's health period as well as 100 data points for each patient's epilepsy period. Variational modal decomposition (VMD) was used to obtain the corresponding intrinsic modal function (VMF) of the data. Then, the differential entropy (DE) and high frequency detection (HFD) of each VMF were extracted as features. The improved grey wolf algorithm is adopted for a selected channel to improve the maximum value of the channel. Finally, the EEG signal samples were classified using a support vector machine (SVM) classifier to achieve the accurate detection of epilepsy EEG signals. Experimental results show that the accuracy, sensitivity and specificity of the proposed method can reach 98.3%, 98.9% and 98.5%, respectively. The proposed algorithm in this paper can be used as an index to detect epileptic seizures and has certain guiding significance for the early diagnosis and effective treatment of epileptic patients.

关键词： epilepsy improved grey wolf algorithm VMD

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Parameters identification of magnetorheological damper based on improved grey wolf optimization algorithm

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PHYSICA SCRIPTA 2025年第1期100卷

作者： Li, Kangjun Yang, Xiaolong Li, Denghui Xie, Guojin Guangxi Univ Sci & Technol Sch Mech & Automot Engn Liuzhou 545006 Peoples R China Univ Guangxi Engn Res Ctr Adv Engn Equipment Nanning Peoples R China

Parameter identification of the mechanical model of magnetorheological dampers is usually carried out through a combination of optimization algorithms, but there is little research on the impact of the optimization algorithm itself on the identification results. In order to improve the accuracy of parameter identification results, the influence of the parameters of the optimization algorithm itself on the fitting results was investigated in this study, and the optimization algorithm adopted the improved grey wolf algorithm. The effects of different wolf pack numbers and iteration times on recognition results were examined in this study. The various parameters in the mechanical model of magnetorheological dampers are determined utilizing the optimal combination of algorithm parameters. Finally, the validity of the identification results was verified by evaluating the consistency between the identified damping force and the experimental damping force. The results indicate that when the optimal combination is used, the accuracy of parameter identification can be improved.

关键词： magnetorheological damper parameter identification improved grey wolf algorithm

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Multi-objective optimization and experimental analysis of rotary tillage parameters for horticultural electric tractors

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 231卷

作者： Huang, Xuekai Wang, Xiaochan Wang, Yanxin Shi, Yinyan Lv, Huawei Zheng, Jinming Chen, Yanyu Nanjing Agr Univ Coll Engn Nanjing 210031 Peoples R China

Rotary tillage is a critical process in agricultural production. However, discrepancies between the chassis movement and the operational parameters of tillage equipment often result in unstable tillage quality, reduced efficiency, and decreased endurance of electric tractors. To address these challenges, this study proposes a multiobjective optimization method that integrates the fuzzy analytic hierarchy process (FAHP) with an improved grey wolf optimizer (IGWO). A tillage quality monitoring system was developed by integrating and processing multi-sensor data to measure the soil fragmentation rate, tillage depth stability coefficient, and power consumption per unit area for various forward speeds, tillage depths, and roller speeds. Regression models were then established based on the collected data. FAHP was employed to determine the influence weights of these models on tillage quality and to construct a multi-objective optimization function. A traversal optimization algorithm based on adaptive dynamic weights and a reverse-learning grey wolf optimization strategy was proposed to identify the optimal working curves for forward and roller speeds under different target tillage depths. The results demonstrate that the proposed algorithm effectively optimizes multi-objective operational parameters, reducing the number of iterations by five and improving accuracy by 20.33% compared to previous methods. Field tests confirmed that the optimal soil fragmentation rate, tillage depth stability coefficient, and power consumption per unit area at different target tillage depths met operational standards for soil fragmentation and tillage depth stability. These findings provide a theoretical foundation and practical guidance for reducing operational tillage resistance and energy consumption in electric horticultural machinery.

关键词： Horticultural electric tractor Tillage operation Parameter matching Multi-objective optimization improved grey wolf algorithm

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A novel multivariate time series combination prediction model

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2024年第7期53卷 2253-2284页

作者： Lian, Lian Tian, Zhongda Shenyang Univ Chem Technol Coll Informat Engn Shenyang 110142 Peoples R China Shenyang Univ Technol Coll Artificial Intelligence Shenyang Peoples R China

This article presents a novel multivariate time series prediction model based on combination model. The input variables of multivariate time series are coupled and correlated. Firstly, kernel principal component analysis is used to preprocess the original multidimensional input variables to determine the principal components that affect the output. Extracting principal components can reduce the complexity of modeling. Then, an improved grey wolf optimization algorithm optimized echo state network is introduced as the prediction model for principal components. Finally, two typical multivariate time series data, air quality index and stock market price, are taken as research objects to verify the performance of the proposed combination prediction model. Compared with other state-of-the-art prediction models, the results clearly reveal that the proposed prediction model is superior to all the considered models herein in terms of both prediction accuracy and performance indicators. As a result, it is concluded that the proposed prediction model can be an efficient and effective technique for multivariate time series prediction.

关键词： Multivariate time series combination prediction model kernel principal component analysis echo state network improved grey wolf algorithm

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Diagnosis of unbalanced rolling bearing fault sample based on adaptive sparse contrative Auto-encoder and IGWO-USELM

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MEASUREMENT 2022年 198卷

作者： Song, Haiquan Ma, Wengang Han, Zhonghe Zhu, Xiaoxun North China Elect Power Univ Sch Energy Power & Mech Engn Beijing Peoples R China Southwest Jiao tong Univ Sch Informat Sci & Technol Chengdu Peoples R China

At present, numerous deep learning techniques are being used in fault diagnosis with good effects. However, obtaining a sample of a fault may be difficult in certain circumstances. Moreover, the distribution of the sample is highly unbalanced. Typically, when the sample size is unbalanced, deep learning methods exhibit the phenomenon of overfitting, which results in a lack of generalizability and precision. In this paper, we proposed an adaptive sparse contractive auto-encoder (ASCAE) and an improved gray wolf optimization unsupervised extreme learning machine (IGWO-USELM) model for diagnosing unbalanced rolling bearing faults. First, CAE was improved by employing sparse graph embedding. Additionally, homotopy regularization was utilized to optimize sparse coefficients. Consequently, the effect of fault feature extraction was improved. The IGWO model was improved using Tent chaotic mapping to compensate for the fault sample's sparse information. Subsequently, we used the IGWO to dynamically optimize USELM's performance. Accordingly, the IGWO-USELM model was developed. The ASCAE was used to extract multi-layer features from the vibration signal. Additionally, the extracted features were fed into the IGWO-USELM fault diagnosis model. Lastly, we performed experimental analysis on the fault dataset from Case Western Reserve University and the two real datasets. The results demonstrate that our method is capable of extracting time-frequency features of high faults. The proposed method is the most accurate when the fault samples are unbalanced. It has demonstrated excellent performance, particularly on the real-world fault dataset. When noise is present, the proposed method exhibits excellent noise immunity and a short run time. A further advantage is that it can be used to perform real-time bearing fault diagnosis.

关键词： Fault diagnosis Adaptive sparse contrative auto-encoder Homotopy regularization Unsupervised extreme learning machine Tent chaotic map improved grey wolf algorithm

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Multi-Step Short-Term Wind Power Prediction Based on Three-level Decomposition and improved grey wolf Optimization

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IEEE ACCESS 2020年 8卷 67124-67136页

作者： Han, Yuchao Tong, Xiangqian Xian Univ Technol Sch Elect Engn Xian 710048 Peoples R China

Wind power prediction is of great importance in enhancing wind energy penetration. This paper proposes a novel wind power prediction method which combining three-level decomposition with optimized prediction method. In the decomposition part, the Wavelet Packet Decomposition (WPD) is introduced as the first level decomposition, then the obtained sub-series are further decomposed by Variable Mode Decomposition (VMD). At last, Singular Spectrum Analysis (SSA) is carried out for each Intrinsic Mode Function (IMF), and the dominant component and residual components are separated as the input of the prediction. In the prediction part, Kernel Extreme Learning Machine (KELM) is adopted to complete the multi-steps wind power prediction. In this paper, an improved grey wolf Optimization (IGWO) algorithm with redesign of the hierarchy and architecture is proposed, which especially suitable for optimizing wind power prediction. Finally, ten different models are compared, and the results show that the proposed method in this paper can extract the trend information of wind power greatly and has achieved excellent accuracy in short-term wind power prediction.

关键词： Wind power prediction three-level decomposition improved grey wolf algorithm multi-step prediction kernel extreme learning machine

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Node coverage optimization algorithm for wireless sensor networks based on improved grey wolf optimizer

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JOURNAL OF algorithmS & COMPUTATIONAL TECHNOLOGY 2019年第13期13卷 -页

作者： Wang, Zhendong Xie, Huamao Hu, Zhongdong Li, Dahai Wang, Junling Liang, Wen Jiangxi Univ Sci & Technol Coll Informat Engn Ganzhou 341000 Jiangxi Peoples R China

Aiming at the problem of wireless sensor network node coverage optimization with obstacles in the monitoring area, based on the grey wolf optimizer algorithm, this paper proposes an improved grey wolf optimizer (IGWO) algorithm to improve the shortcomings of slow convergence, low search precision, and easy to fall into local optimum. Firstly, the nonlinear convergence factor is designed to balance the relationship between global search and local search. The elite strategy is introduced to protect the excellent individuals from being destroyed as the iteration proceeds. The original weighting strategy is improved, so that the leading wolf can guide the remaining grey wolves to prey in a more reasonable way. The design of the grey wolf's boundary position strategy and the introduction of dynamic variation strategy enrich the population diversity and enhance the ability of the algorithm to jump out of local optimum. Then, the benchmark function is used to test the convergence performance of genetic algorithm, particle swarm optimization, grey wolf optimizer, and IGWO algorithm, which proves that the convergence performance of IGWO algorithm is better than the other three algorithms. Finally, the IGWO algorithm is applied to the deployment of wireless sensor networks with obstacles (rectangular obstacle, trapezoidal obstacle and triangular obstacles). Simulation results show that compared with GWO algorithm, IGWO algorithm can effectively improve the coverage of wireless sensor network nodes and obtain higher coverage rate with fewer nodes, thereby reducing the cost of deploying the network.

关键词： Wireless sensor network coverage optimization improved grey wolf algorithm coverage

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