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Using the group method for the synthesis of copper/ZrO2 nanocomposites to achieve high wear resistance by ball milling and spark plasma sintering

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CERAMICS INTERNATIONAL 2022年第12期48卷 17576-17588页

作者： Shojaei, Mohammdreza Hasani, Atefe Amiri, Zahra Khayati, Gholam Reza Sharif Univ Technol Dept Mat Sci & Engn Tehran Iran Shahid Bahonar Univ Kerman Dept Mat Sci & Engn POB 76135-133 Kerman Iran

The use of ZrO2 nanoparticles is common in the Cu matrix to manufacture Cu/ZrO2 nanocomposites. Many approaches and parameters produce Cu/ZrO2 nanocomposites with an appropriate wear rate and other properties. Hence, proposing an accurate and reliable model was imperative. The main goal of this paper is to find out the best models among the ant colony optimization (ACO), gene expression programming (GEP), artificial bee colony (ABC), and gray wolf optimization algorithm (GWOA) to predict the wear rate of/ZrO2 nanocomposites. To the best of our knowledge, this is the first research that predicts Cu/ZrO2 nanocomposites wear rate using group modeling. To develop models, 105 data were collected from the Cu/ZrO2 manufacturing by ball milling and spark plasma sintering in different conditions. The input parameters are Cu and ZrO2 concentration, milling time, and sintering temperature. The proposed rules for the prediction of wear rate based on the collected dataset are assessed based on several metrics such as (R square), mean absolute error percentage (MAPE), root mean square error (RRSE), mean square error (MSE), relative error and average absolute relative. According to the results, the ACO enables to predict wear rate by R square = 0.9923, MAPE = 5.2, RRSE = 0.0143, and MSE = 0.0043. Also, the sensitivity analysis results revealed that the milling time and ZrO2 content are the most effective parameters on the wear rate of Cu/ZrO2 nanocomposites. Moreover, experiments were done at the best model to prove the validity of the ACO. According to the results, it can be concluded that the ACO model and real analysis are fully functional to accurately estimate the optimal conditions for ball milling conditions in the production of Cu/ZrO2 nanocomposites.

关键词： Cu/ZrO2 nanocomposite Spark plasma sintering Modeling Ant colony optimization Gene expression programming Artificial bee colony gray wolf optimization algorithm

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DC-DC circuit fault diagnosis based on GWO optimization of 1DCNN-GRU network hyperparameters

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ENERGY REPORTS 2023年 9卷 536-548页

作者： Li, Zhen-Bi Feng, Xue-Yan Wang, Li Xie, Yi-Chen Bozhou Univ Dept Elect Bozhou 236000 Peoples R China Anhui Univ Sci & Technol Sch Elect & Informat Engn Huainan 232000 Peoples R China

Aiming at the problem that traditional machine learning methods rely on manual feature extraction in DC-DC circuit soft faults, it is important to effectively obtain the fault characteristics of DC-DC circuit soft faults. In this study, combining the advantages of one-dimensional convolutional neural network (1DCNN) and gated logic unit (GRU), a deep learning model for fault identification of time series signals is proposed to realize soft fault diagnosis of DC-DC circuits. In this study, a 1DCNN-GRU network model is constructed, and 1DCNN can directly perform automatic feature extraction on the data, while the GRU makes up for the shortcomings of CNN in processing time series data, thereby ensuring the comprehensiveness of the extracted features. For the hyperparameter problem in the network model, the powerful optimization ability of the gray wolf optimization algorithm is used to automatically search for the best hyperparameters in the 1DCNN-GRU network, and then the optimized 1DCNN-GRU network model is used for comprehensive feature learning. In order to meet the needs of deep learning for large data samples, the overlapping sampling method is used to enrich the data sample set. Experimental results show that the proposed method achieves 99.62% accuracy in DC-DC circuit fault diagnosis, and still maintains good robustness in noisy environment.(c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Network hyperparameters gray wolf optimization algorithm 1DCNN-GRU Circuit fault diagnosis

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A nutrient optimization method for hydroponic lettuce based on multi-strategy improved grey wolf optimizer algorithm

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 224卷

作者： Zhang, Xihai Xia, Juheng Chen, Zerui Zhu, Jiaxi Wang, Hao Northeast Agr Univ Coll Elect & Informat Harbin 150030 Peoples R China Northeast Agr Univ Natl Key Lab Smart Farm Technol & Syst Harbin 150030 Peoples R China

Hydroponic agriculture, as a modern agricultural technology, not only enables crops to better realize their growth potential but also frees them from the traditional agricultural constraints of land resources. Currently, the nutrient solutions for hydroponic crops are mostly prepared using generic formulas, lacking precise ratios based on the specific needs of different crop types and growth stages. It is of great significance to study the precise nutrient requirements of specific crops at different growth stages to enhance yield. To this end, a multi-strategy improved Grey wolf optimization algorithm is proposed in this paper to optimize the fertilizer model for hydroponic lettuce at different growth stages, providing more accurate nutrient requirements for hydroponic lettuce at different growth stages. Firstly, to solve the problems of the traditional GWO algorithm, such as insufficient exploration and the tendency to fall into local optima, non-linear functions, Symbiotic Organisms Search (SOS), and Opposition-Based Learning (OBL) were integrated into the GWO algorithm. Secondly, the objective was to minimize the residual function of the nitrogen, potassium, and calcium ternary fertilizer effect equation, the proposed algorithm was used to optimize the coefficients of the equation. Lastly, the optimal concentrations of NO3-, K+, and Ca2+ for each growth stage of lettuce were calculated based on the optimized fertilizer effect equations. The proposed algorithm was compared with five other excellent metaheuristic algorithms in optimizing fertilizer model coefficients. Experimental results indicate that the proposed algorithm outperforms the other algorithms used in the experiments. For the nutrient models of three growth stages, the proposed algorithm achieves improvements compared to the GWO algorithm, with R-2 increasing by 0.73 %, 0.66 %, and 0.51 %, and RMSE decreasing by 8.53 %, 5.70 %, and 3.83 % respectively. This contributes to providing more accurate nutri

关键词： Hydroponic agriculture Nutrient optimization Multi-strategy improvement gray wolf optimization algorithm

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The defect of the Grey wolf optimization algorithm and its verification method

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KNOWLEDGE-BASED SYSTEMS 2019年第May 1期171卷 37-43页

作者： Niu, Peifeng Niu, Songpeng Liu, Nan Chang, Lingfang Yanshan Univ Inst Elect Engn Qinhuangdao 066004 Hebei Peoples R China

Grey wolf optimization algorithm (GWO) is a new meta-heuristic optimization technology. Its principle is to imitate the behavior of grey wolves in nature to hunt in a cooperative way. GWO is different from others in terms of model structure. It is a large-scale search method centered on three optimal samples, and which is also the research object of many scholars. In the course of its research, this paper find that GWO is flawed. It has good performance for the optimization problem whose optimal solution is 0, however, for other problems, its advantage is not as obvious as before or even worse. Then it is further found that when GWO solves the same optimization function, the farther the function's optimal solution is from 0, the worse its performance, and this flaw also appears in other optimization algorithms. Through the study of this defect, the analysis is carried out, and the reason is determined. Finally, although there is no way to make GWO normal, this paper provides a verification method to avoid the same problem, and hopes to help the development of the optimization algorithm. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Meta-heuristic gray wolf optimization algorithm optimization algorithm Defect New verification method

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Abnormal State Detection of OLTC Based on Improved Fuzzy C-means Clustering

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Chinese Journal of Electrical Engineering 2023年第1期9卷 129-141页

作者： Hongwei Li Lilong Dou Shuaibing Li Yongqiang Kang Xingzu Yang Haiying Dong School of Automation and Electrical Engineering Lanzhou Jiaotong UniversityLanzhou 730070China School of New Energy and Power Engineering Lanzhou Jiaotong UniversityLanzhou 730070China

An accurate extraction of vibration signal characteristics of an on-load tap changer(OLTC)during contact switching can effectively help detect its abnormal ***,an improved fuzzy C-means clustering method for abnormal state detection of the OLTC contact is ***,the wavelet packet and singular spectrum analysis are used to denoise the vibration signal generated by the moving and static contacts of the ***,the Hilbert-Huang transform that is optimized by the ensemble empirical mode decomposition(EEMD)is used to decompose the vibration signal and extract the boundary spectrum ***,the gray wolf algorithm-based fuzzy C-means clustering is used to denoise the signal and determine the abnormal states of the OLTC *** analysis of the experimental data shows that the proposed secondary denoising method has a better denoising effect compared to the single denoising *** EEMD can improve the modal aliasing effect,and the improved fuzzy C-means clustering can effectively identify the abnormal state of the OLTC *** analysis results of field measured data further verify the effectiveness of the proposed method and provide a reference for the abnormal state detection of the OLTC.

关键词： On-load tap changer singular spectrum analysis Hilbert-Huang transform gray wolf optimization algorithm fuzzy C-means clustering

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A modified multi-stepped constant current based on gray wolf algorithm for photovoltaics applications

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ELECTRICAL ENGINEERING 2024年第4期106卷 3853-3867页

作者： Dagal, Idriss Akin, Burak Dari, Yaya Dagal Yildiz Tech Univ Fac Elect Engn Elect Engn Istanbul Turkiye Yildiz Tech Univ Elect Engn C 318 Davutpasa Campus Istanbul Turkiye Inst Natl Super Sci & Tech Abeche INSTA Abeche Chad

One of the most known and usable renewable energy is photovoltaic solar energy. Its properties such as recursivity, cleanness, limitlessness, and reliability make it the most useful source of energy. Some undesirable natural (irradiance and temperature) variation affects the photovoltaic solar modules' characteristics. However, photovoltaic PV solar power needs to be monitored accurately, effectively, and efficiently in order to keep the global maximum power point (GMPP) steady. Therefore, this paper presents a modified multi-stepped constant current based on the gray wolf optimization (MMSCC-GWO) algorithm for photovoltaic systems. The proposed technique utilizes a multi-step constant current to the pack of wolves by monitoring the inside and the outside step of the hierarchical wolves. This proposed (MMSCC-GWO) algorithm accurately tracks the maximum power point from the photovoltaic systems under different partial shading conditions (PSCs). A comparative result with other conventional methods is investigated and the simulation results carried out in the MATLAB/Simulink environment reveal that the proposed approach outperforms the existing techniques in terms of efficiency, tracking time, and accuracy.

关键词： Efficiency gray wolf optimization algorithm Multi-stepped constant current Partial shading

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Illumination correction model with sine-cosine algorithm to optimize gray wolf population for least-squares support vector regression

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COLOR RESEARCH AND APPLICATION 2022年第1期47卷 92-106页

作者： Yang, Junyi Zhao, Haitao Chen, Sheng Hangzhou Dianzi Univ Sch Mech Engn Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Sch Elect & Informat Engn Hangzhou Peoples R China

The color of an object appears different from its true color when illuminated with light sources of different hues. To solve this problem, this article proposes a combination algorithm (SCA-GWO-LSSVR) based on the sine-cosine algorithm (SCA) and the gray wolf optimization (GWO) algorithm to optimize the regression prediction model of the least-squares support vector regression (LSSVR) algorithm. The performance of the traditional LSSVR is significantly affected by the penalty parameter (gamma) and the sig2 kernel function parameter. The proposed method uses the improved GWO algorithm to search the population to find the best LSSVR parameter solution. The proposed algorithm uses the SCA to create multiple random candidate solutions in population initialization to avoid blind initialization of the GWO algorithm. In the process of iterative optimization, the SCA is infiltrated, and its sine-cosine wave mathematical model is used to quickly identify the best outward or inward position of the gray wolf. Finally, the LSSVR combines the optimal sig2 kernel function parameters and penalty parameters (gamma) to obtain a highly versatile illumination correction model. The experimental results show that the fitting accuracy of the proposed method reaches 86.8%, which is 5% higher than that of the LSSVR algorithm alone.

关键词： color constancy gray wolf optimization algorithm least-squares support vector regression sine-cosine algorithm

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Surface roughness prediction and process parameter optimization of Ti-6Al-4 V by magnetic abrasive finishing

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第1期122卷 219-233页

作者： Song, Zhuang Zhao, Yugang Liu, Guangxin Gao, Yuewu Zhang, Xiajunyu Cao, Chen Dai, Di Deng, Yueming Shandong Univ Technol Inst Adv Mfg Zibo 255049 Peoples R China Shandong Univ Technol Sch Mech Engn 266 Xincun West Rd Zibo 255049 Peoples R China

In order to effectively predict the surface roughness Ra of Ti-6Al-4 V material after magnetic abrasive finishing (MAF) process, and optimize the process parameters to improve the surface quality of the material. Firstly, diamond/Fe-based magnetic abrasive powders (MAPs) are prepared for the MAF process of Ti-6Al-4 V by using the gas-solid two-phase double-stage atomization and rapid solidification method. The effects of rotational speed of the magnetic pole, working gap, feed velocity of workpiece, and filling quantity of MAPs on the surface roughness efficiency are discussed. Secondly, the orthogonal experiment is designed. The prediction model of surface roughness based on gray wolf optimization (GWO) algorithm and support vector regression (SVR), which is constructed according to the experimental results. The simulation shows that the R-2 of the optimized prediction model is 0.987456, and the MAPE is less than 1.99%. Finally, GWO algorithm is employed again to perform a global optimization search on the constructed prediction model. The optimal combination of process parameters is searched and verified, the surface roughness Ra is 0.098 mu m, and the relative error is less than 2.82% compared with the model prediction. The comparison of surface morphology before and after MAF of Ti-6Al-4 V shows that the MAF technology combined with the prediction model based on GWO-SVR can effectively improve the surface quality of Ti-6Al-4 V.

关键词： Magnetic abrasive finishing Ti-6Al-4 V gray wolf optimization algorithm Support vector regression Surface roughness

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Modified Tuned Liquid Dampers for Seismic Protection of Buildings Considering Soil-Structure Interaction Effects

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING 2020年第1期44卷 339-354页

作者： Kamgar, Reza Gholami, Fatemeh Sanayei, Hamed Reza Zarif Heidarzadeh, Heisam Shahrekord Univ Dept Civil Engn POB 88186-3414 Shahrekord Iran

Considering the effect of soil-structure interaction in dynamic analysis of structures can change their responses. It is generally assumed that the structure is located on a rigid foundation and the flexibility effect of the soil is not considered. Researches on the soil-structure interaction show that the dynamic response of the structures located on a soft and flexible soil is completely different from the dynamic response of the same structure located on a stiff soil. In this paper, the effect of the soil-structure interaction on the response of a single-degree-of-freedom system (Nagasaki airport tower) that is controlled by a modified tuned liquid damper is investigated. The soil effect is modeled using an approximate cone method based on the semi-infinite boundary conditions. First, the governing equations for describing the fluid sloshing obtained with shallow water wave theory are solved by Lax's finite-difference scheme. Then, the dynamic equilibrium equations for a structure controlled with a modified tuned liquid damper are obtained by considering the effect of soil-structure interaction using Lagrange's method. These equations are solved numerically by Newmark's method. The controlled structural responses are calculated in different time steps and compared with the responses of the uncontrolled structure. Results show that the seismic design of the modified tuned liquid damper system can be more effective to reduce the structural responses. Also, this system can reduce efficiently the maximum responses of the structures considering soil-structure interaction effect during a near-fault earthquake.

关键词： optimization Soil-structure interaction Modified tuned liquid damper gray wolf optimization algorithm Seismic protection

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Integrated diagnosis optimization design of the electronic equipment based on spatial mapping

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SCIENCE PROGRESS 2024年第4期107卷 368504241285770页

作者： Gu, Xu-ping Shi, Xian-Jun Naval Aviat Univ Coll Coastal Def Force 188 Erma Rd Yantai 264001 Shandong Peoples R China

The complexity of test and fault information within electronic devices makes their integrated diagnosis a challenging problem when designing equipment reliability. Current integrated diagnosis is analyzed for test optimization and test resource optimization. However, this neglects the connection between them. This paper proposes a design strategy for integrated diagnosis optimization based on the spatial mapping principle to quantitatively describe the constraint relationship between them. The integrated diagnosis optimization model is established by constructing the logical mapping relationship between test space, resource space, and fault space, and the optimal test configuration and test resource configuration are sought based on the grey wolf optimization algorithm. Seven high-dimensional benchmark functions and an integrated diagnosis model of electronic equipment are used to verify the efficiency of the algorithm proposed in this paper. The proposed algorithm is compared with the other four in terms of the algorithm's optimization speed and accuracy. The results indicate that the electronic equipment after integrated diagnosis optimization has critical fault detection, fault detection, fault isolation, and false alarm rates of 100%, 99.99%, 98.99%, and 0.2993%, respectively. After the integrated diagnosis optimization, the number of tests of the equipment is reduced by 88.9%, and the test cost is saved by 89%. Compared with the other algorithms, grey wolf optimization achieves the best optimization results, reduces the number of tests by 42%-55%, and decreases the test cost by 77.63%-83.91%. This strategy not only considers the test optimization of equipment and test resources optimization but also dramatically reduces the test cost while improving the test efficiency.

关键词： Integrated diagnosis test optimization resource optimization hierarchical analysis gray wolf optimization algorithm

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