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Two-stage deep learning hybrid framework based on multi-factor multi-scale and intelligent optimization for air pollutant prediction and early warning

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2022年第10期36卷 3417-3437页

作者： Wang, Jujie Xu, Wenjie Dong, Jian Zhang, Yue Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Forecast & Evaluat Meteoro Nanjing 210044 Peoples R China

Effective prediction of air pollution concentrations is of great importance to both the physical and mental health of citizens and urban pollution control. As one of the main components of air pollutants, accurate prediction of PM2.5 can provide a reference for air pollution control and pollution warning. This study proposes an air pollutant prediction and early warning framework, which innovatively combines feature extraction techniques, feature selection methods and intelligent optimization algorithms. First, the PM2.5 sequence is decomposed into several subsequences using the complete ensemble empirical mode decomposition with adaptive noise, and then the new components of the subsequences with different complexity are reconstructed using fuzzy entropy. Then, the Max-Relevance and Min-Redundancy method is used to select the influencing factors of the different reconstructed components. Then, a two-stage deep learning hybrid framework is constructed to model the prediction and nonlinear integration of the reconstructed components using a long short-term memory artificial neural network optimized by the gray wolf optimization algorithm. Finally, based on the proposed hybrid prediction framework, effective prediction and early warning of air pollutants are achieved. In an empirical study in three cities in China, the prediction accuracy, warning accuracy and prediction stability of the proposed hybrid framework outperformed the other comparative models. The analysis results indicate that the developed hybrid framework can be used as an effective tool for air pollutant prediction and early warning.

关键词： Feature extraction Feature selection grey wolf optimization algorithm Two-stage intelligent optimization Air pollutant prediction and early warning

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Swarm-Based Extreme Learning Machine Models for Global optimization

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Computers, Materials & Continua 2022年第3期70卷 6339-6363页

作者： Mustafa Abdul Salam Ahmad Taher Azar Rana Hussien Artificial Intelligence Department Faculty of Computers and Artificial IntelligenceBenha UniversityBenha13518Egypt Scientific Computing Department Faculty of Computers and Artificial IntelligenceBenha UniversityBenha13518Egypt

Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ***,Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence,high time and space *** ELM,the hidden layer typically necessitates a huge number of ***,there is no certainty that the arrangement of weights and biases within the hidden layer is *** solve this problem,the traditional ELM has been hybridized with swarm intelligence optimization *** paper displays five proposed hybrid algorithms“Salp Swarm algorithm(SSA-ELM),Grasshopper algorithm(GOA-ELM),grey wolf algorithm(GWO-ELM),Whale optimizationalgorithm(WOA-ELM)andMoth Flame optimization(MFO-ELM)”.These five optimizers are hybridized with standard ELM methodology for resolving the tumor type classification using gene expression *** proposed models applied to the predication of electricity loading data,that describes the energy use of a single residence over a fouryear *** the hidden layer,Swarm algorithms are used to pick a smaller number of nodes to speed up the execution of *** best weights and preferences were calculated by these algorithms for the hidden *** results demonstrated that the proposed MFO-ELM achieved 98.13%accuracy and this is the highest model in accuracy in tumor type classification gene expression *** in predication,the proposed GOA-ELM achieved 0.397which is least RMSE compared to the other models.

关键词： Extreme learning machine salp swarm optimization algorithm grasshopper optimization algorithm grey wolf optimization algorithm moth flame optimization algorithm bio-inspired optimization classification model and whale optimization algorithm

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Frequency stabilization of a hybrid three-area power system equipped with energy storage units and renewable energy sources

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IET RENEWABLE POWER GENERATION 2022年第15期16卷 3267-3286页

作者： Elsaied, Mohamed Mostafa Abdel Hameed, Walid Helmy Hasanien, Hany M. Ain Shams Univ Elect Power & Machines Dept Cairo Egypt

This paper presents a three-area system tied together with tie lines. During the intervals of power mismatch, the system frequency deviates beyond the nominal value with an oscillatory response and the system may go to instability mode. The main purpose of the control strategy is optimizing the frequency fluctuations of the three areas and the deviations in the three tie line powers. This is achieved by three controllers as follows: the Tilt-Integral-Derivative (TID) controller, the Fractional Order Proportional-Integral-Derivative (FOPID), and the Proportional-Integral-Derivative (PID) controller. The three controllers are optimized by a new metaheuristic optimization algorithm based on the jellyfish behaviour in the ocean called the Jellyfish Search (JS) Optimizer. To prove the algorithm validity, it is compared with previous optimization techniques that have been applied to the study field such as grey wolf optimization (GWO) algorithm and Genetic algorithm (GA). Furthermore, renewable energy sources are implemented in the system such as wind energy and photovoltaic based on real data. Finally, energy storage devices (ESDs) like superconducting magnetic energy storage (SMES), capacitor energy storage (CES), and battery energy storage (BES) are implemented to improve the system behaviour due to the intermittent behaviour in the renewable sources.

关键词： three-term control power generation control renewable sources control strategy previous optimization techniques energy storage devices instability mode Proportional-Integral-Derivative controller system frequency deviates optimisation Fractional Order Proportional-Integral-Derivative oscillatory response energy storage frequency fluctuations control system synthesis tie line powers metaheuristic optimization algorithm power mismatch metaheuristics Jellyfish Search Optimizer capacitor energy storage tie lines algorithm validity frequency control Genetic algorithm system behaviour battery energy storage frequency stabilization superconducting magnet energy storage three-area system battery storage plants Tilt-Integral-Derivative controller renewable energy sources energy storage units three-area power system grey wolf optimization algorithm jellyfish behaviour genetic algorithms

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Estimation of permeability from a hydrocarbon reservoir located in southwestern Iran using well-logging data and a new intelligent combined method

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CARBONATES AND EVAPORITES 2023年第1期38卷 1-14页

作者： Nazari, Hosnie Hajizadeh, Farnusch Urmia Univ Dept Min Engn Orumiyeh Iran

The most important tools of reservoir management are "description of reservoir properties " and "reservoir simulation ", among which permeability is the most important factor for accurate description and modeling of the reservoir. Standard method for determining permeability in oil industry is core analysis and well testing. These methods are very expensive and not all wells in a field have cores. As a result, the methods applicable to present the petrophysical properties of reservoir, including permeability and well-logging, are highly important because well logs are usually available for all wells in a field. Artificial intelligence methods are new, low-cost and accurate methods that can indirectly estimate the permeability of reservoir in the shortest possible time using well-logging data. Therefore, in this study, using different well logs and a new intelligent combined method of relevance vector regression with grey wolf optimization (RVR-GWO) algorithm, the permeability of a hydrocarbon reservoir in southwestern Iran (Azadegan oil field) was indirectly estimated. Then, the performance of this hybrid model was compared with that of relevance vector regression (RVR) method. The database consisted of 2506 well-logging data, which were divided into two categories of training data (1754 data) and test data to evaluate the models (752 data). The results showed very good performance of the combined method of RVR-GWO algorithm in estimating permeability. Therefore, this model can be used as a powerful, fast, and accurate method for indirect estimation of permeability in reservoirs where permeability through the core is not measured.

关键词： Permeability Well-logging data Relevance vector regression grey wolf optimization algorithm Hydrocarbon reservoir

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Hybrid machine learning approach for accurate prediction of the drilling rock index

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SCIENTIFIC REPORTS 2024年第1期14卷 1-16页

作者： Shahani, Niaz Muhammad Zheng, Xigui Wei, Xin Jiang, Hongwei China Univ Min & Technol Sch Mines Xuzhou 221116 Peoples R China China Univ Min & Technol State Key Lab Geo Mech & Deep Underground Engn Xuzhou 221116 Peoples R China Liupanshui Normal Univ Sch Mines & Civil Engn Liupanshui Peoples R China Guizhou Guineng Investment Co Ltd Guiyang Peoples R China

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 (R-2) 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 R-2 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.

关键词： Drillability Drilling rate index XGBoost Mining engineering grey wolf optimization algorithm

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GWO-Potential Field Method for Mobile Robot Path Planning and Navigation Control

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第8期46卷 8087-8104页

作者： Zafar, Mohd Nayab Mohanta, J. C. Keshari, Anupam MNNIT Allahabad Dept Mech Engn Prayagraj India Natl Inst Ind Engn Mumbai Maharashtra India

A navigation control methodology is proposed in this paper, which utilizes a hybrid concept of grey wolves optimisation (GWO) algorithm and an artificial potential field (APF) method for on-time path planning of mobile robot. The proposed methodology runs in two folds. The first fold defines the focus region (FR), which shows the obstacle-free locations of the all possible robot movements. However, in the second fold step GWO algorithm searches the shortest path by minimizing the artificial potential field value of the location generated within FR. The presented methodology was simulated and verified in various real and simulation environments. The simulation and experimental results reveal that the presented methodology can not only be capable of finding an optimal or near-optimal robot-path in a complex obstacle-present environment but also provides an effective path planning strategy on-time basis. The results show that the proposed method has out-performed to shorten the path length as well as ensured collision-free navigation. At the same time, virtual intermediate targets (VITs) makes the navigation free from any dead-end situation, even in a cluttered environment.

关键词： Mobile robot navigation grey wolf optimization algorithm Artificial potential field Collision avoidance

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grey wolf based compressive sensing scheme for data gathering in IoT based heterogeneous WSNs

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WIRELESS NETWORKS 2020年第5期26卷 3395-3418页

作者： Aziz, Ahmed Osamy, Walid Khedr, Ahmed M. El-Sawy, Ahmed A. Singh, Karan Benha Univ Fac Comp & Artificial Intelligence Dept Comp Sci Banha Egypt Univ Sharjah Dept Comp Sci Sharjah 27272 U Arab Emirates Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi India Sharda Univ Fac Engn & Technol Andijon Uzbekistan Qassim Univ Coll Community Unaizah Dept Appl Sci POB 931 Buridah 51931 Unizah Saudi Arabia Zagazig Univ Math Dept Zagazig Egypt

Sensor node energy constraint is considered as an impediment in the further development of the Internet of Things (IoT) technology. One of the most efficient solution is to combine between compressive sensing (CS) and routing techniques. However, this combination faces many challenges that makes it an attractive point for research. This paper proposes an Efficient Multi-hop Cluster-based Aggregation scheme using Hybrid CS (EMCA-CS) for IoT based heterogeneous wireless sensor networks (WSNs). EMCA-CS efficiently combines between CS and routing protocols to extend the network lifetime and reduces the reconstruction error. EMCA-CS includes the following: a new algorithm to partition the field into various hexagonal cells (clusters) and based on multiple criteria, selects a node from each cluster as cluster head (CH). Each CH will then compress its cluster data using hybrid CS method. Also, a new grey wolf based algorithm to create optimal path for CHs to deliver the compressed data to base station (BS) and a CSMO-GWO algorithm to optimize the CS matrix construction process is introduced. Moreover, a new grey wolf and reversible Greedy based Reconstruction algorithm is proposed to recover the actual data. The simulation results indicate that the performance of the proposed work exceeds the existing baseline techniques in terms of prolonging WSN lifetime, reducing the power consumption and reducing normalized mean square error.

关键词： Compressive sensing Cluster-based Energy consumption grey wolf optimization algorithm Network lifetime Internet of Things Routing techniques Wireless sensor networks

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Real-time Driving Ability Prediction Model Based on GWO-LSTM 5

Real-time Driving Ability Prediction Model Based on GWO-LSTM

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5th International Conference on Mechatronics Technology and Intelligent Manufacturing (ICMTIM)

作者： Chen, Yongqing Xia, Yangyang Chen, Jiajia Chen, Jie Anhui Transportat Holding Grp Co Ltd Hefei Anhui Peoples R China Jiangsu Univ Sch Automot & Traff Engn Zhenjiang Jiangsu Peoples R China

ISBN: (纸本)9798350363272;9798350363265

With the development of intelligent transportation system, accurate prediction of driver's real-time driving ability has become the key to ensure road traffic safety and improve driving efficiency. This paper proposes a real-time driving ability prediction model based on grey wolf optimization algorithm (GWO) and long short-term memory neural network (LSTM). The model optimizes the network parameters of LSTM through GWO, improves the prediction accuracy and generalization ability, and compares it with support vector regression (SVR) and classification regression tree (CART) prediction models. The experimental results show that the average coefficient of determination of the GWO-LSTM model is 0.9949, which is greater than 0.9840 of the SVR model and 0.9602 of the CART model. The model has good performance in real-time driving ability prediction and provides strong support for the development of intelligent transportation systems.

关键词： driving behavior risk real-time driving ability prediction grey wolf optimization algorithm long short-term memory neural network

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grey wolf algorithm with Borda Count for Feature Selection in Classification 3

Grey Wolf Algorithm with Borda Count for Feature Selection i...

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3rd International Conference on Control and Robotics Engineering (ICCRE)

作者： Jitkongchuen, Duangjai Phaidang, Pongsak Dhurakij Pundit Univ Coll Innovat Technol & Engn Bangkok Thailand

ISBN: (纸本)9781538666630

This research applies the grey wolf optimization algorithm which includes the feature selection stage and implements borda count method to optimize the function selection problem in classification. The grey wolf optimization mimics the characteristics and movement of wolves which have more than one leader in a pack. The proposed algorithm presents the pack can have more than one pack which selects the most relevant features. The performance of proposed algorithm is compared with other classification techniques such as cAnt-Miner, C4.5 and PART. The experimental results show that the proposed algorithm is capable to optimized feature selection in classification problems.

关键词： classification feature selection grey wolf optimization algorithm borda count method

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Type recognition of partial discharge source based on PCA and GWO-SVM 33

Type recognition of partial discharge source based on PCA an...

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33rd Chinese Control and Decision Conference (CCDC)

作者： Li Li Chen Yuepeng Yang Guang Mao Cuimin Zhang Huajun Wuhan Univ Technol Sch Automat Wuhan 430070 Hubei Peoples R China Hubei Elect Power Survey & Design Inst Co LTD Wuhan 430040 Hubei Peoples R China

ISBN: (纸本)9781665440899

As an important indication and manifestation of insulation failures in high-voltage equipment, the type identification of partial discharge (PD) is important for the assessment of the insulation state of electrical equipment. In order to identify the type of partial discharge source accurately, this paper presents a method for partial discharge source type identification based on principal component analysis (PCA) and support vector machine (SVM) with grey wolf optimization algorithm (GWO). The PCA method is used to select the three features out of 10 that best represent the original data, namely the phase, E/I-peak and I-peak/Q. On this basis, the SVM kernel function g and the non-negative penalty factor c are optimized by GWO to establish a support vector machine classification model based on the grey wolf optimization algorithm (GWO-SVM). The results show that PCA is able to extract the main features well and that GWO-SVM can identify partial discharge source more accurately than the genetic algorithm-based support vector machine (GA-SVM), multilayer perceptron (MLP) and k-nearest neighbor (kNN) classifier.

关键词： Principal component analysis support vector machines identification of partial discharge types grey wolf optimization algorithm k-Nearest neighbors multilayer perceptron

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