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Multi-Strategy Improved northern goshawk optimization algorithm and Application

IEEE ACCESS 2024年 12卷 34247-34264页

作者： Zhang, Fan Jinan Univ Coll Informat Sci & Technol Dept Math Guangzhou 510632 Guangdong Peoples R China

The northern goshawk optimization algorithm (NGO) is a population-based meta-heuristic algorithm inspired by the hunting behavior of the northern goshawk. Compared with other algorithms, NGO have a certain competitiveness, but there is still an imbalance in development and exploration, and it is easy to fall into the local optimal. This paper proposes a multi-strategy Improved northern goshawk optimization algorithm (INGO) to address these shortcomings. INGO uses an improved tent chaos mapping strategy to generate the initial population and improve the quality of the initial solution set. The levy flight strategy is introduced in the hunting stage of the NGO to improve the search range of the solution and avoid the algorithm's prematurity. In addition, we introduce a nonlinear convergence factor and heart-shaped search strategy to reduce the probability of the algorithm falling into the local optimal and improve the convergence speed of the algorithm. We evaluated INGO's performance using 23 benchmark functions, CEC2017 test suite functions, and three constrained engineering optimization problems and validated its optimization using Wilcoxon rank sum tests. Experimental results show that the algorithm has higher convergence accuracy and better detection ability. Finally, INGO was applied to optimize the ensemble learning system.

关键词： northern goshawk optimization algorithm metaheuristics levy flight ensemble learning CEC2017

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Enhanced Iterative Closest Point algorithm Based on an Improved northern goshawk optimization algorithm and Random Sampling 24

Enhanced Iterative Closest Point Algorithm Based on an Impro...

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2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition (CVIPPR)

作者： Li, Ke Fu, Shengwei Huang, Haisong Fan, Qingsong Guizhou Univ Minist Educ Key Lab Adv Mfg Technol Guiyang Peoples R China

ISBN: (纸本)9798400716607

To address the issues of inadequate accuracy and low memory efficiency in 3D point cloud registration, we propose a targeted optimization scheme (ENGO_ICP). This scheme integrates random sampling technology with an enhanced northern goshawk optimization algorithm, which is developed through multi-strategy fusion. Our objective is to improve the coarse registration phase of the iterative closest point (ICP) algorithm, which is notably sensitive to the initial transformation matrix. We partition the registration process into two main phases: coarse registration and fine registration. For coarse registration, a random sampling algorithm is employed to reduce the number of point clouds involved, thereby accelerating the speed of preliminary registration. Subsequently, we introduce an enhanced northern goshawk optimizer (ENGO) that boosts the algorithm's search capability and convergence speed by incorporating a leader-based adaptive Brownian motion strategy, nonlinear control parameters, and a leader-focused boundary control strategy. This optimizer constructs search individuals using translation and rotation transformation parameters, facilitating high-quality initial poses for the subsequent fine registration of the ICP point cloud. To validate our method's effectiveness, we conducted simulation experiments using the FGR dataset. Our method's performance is compared against classical point cloud registration algorithms, including ICP, TrICP, GWO_ICP, and NGO_ICP, using the root mean square error (RMSE) as the evaluation metric. The experimental results demonstrate superior accuracy of our proposed method.

关键词： 3D point cloud registration northern goshawk optimization algorithm Iteration closest point Random sampling

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Optimal Scheduling of Distribution Network Maintenance Personnel Based on The northern goshawk optimization algorithm 12

Optimal Scheduling of Distribution Network Maintenance Perso...

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IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)

作者： Liu, Chengwei Chen, Chaoyang Li, Pei Chen, Zuguo Hunan Univ Sci & Technol Xiangtan 411100 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

ISBN: (纸本)9798350321050

Extreme events have caused large-scale power disruptions across the planet in recent years. Extreme events can cause multiple faults in the distribution network, maintenance personnel must be sent out to fix the faulty lines since the distribution network cannot be repaired in its entirety through islanding and reconfiguration. However, the number of faulty lines often exceeds the amount of the maintenance workforce, and there are also variations in the maintenance times of individual maintenance personnel. How to schedule maintenance personnel in a rational manner to minimize the total economic cost is the problem to be addressed in this study. Firstly, in order to model load uncertainty, Monte Carlo approach is utilized to model various scenarios. Secondly, the modified northern goshawk optimization algorithm is used to solve the best scheduling scheme. Finally, the effectiveness of the proposed method is verified by concrete examples.

关键词： distribution network fault recovery personnel scheduling northern goshawk optimization algorithm

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Oral cancer diagnosis based on gated recurrent unit networks optimized by an improved version of northern goshawk optimization algorithm

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HELIYON 2024年第11期10卷 e32077页

作者： Zhang, Lei Shi, Rongji Youssefi, Naser Shandong Univ Hosp 2 Cheeloo Coll Med Dept Stomatol Jinan 250033 Shandong Peoples R China Islamic Azad Univ Sci & Res Branch Tehran Iran Islamic Univ Coll Tech Engn Najaf Iraq

Oral cancer early diagnosis is a critical task in the field of medical science, and one of the most necessary things is to develop sound and effective strategies for early detection. The current research investigates a new strategy to diagnose an oral cancer based upon combination of effective learning and medical imaging. The current research investigates a new strategy to diagnose an oral cancer using Gated Recurrent Unit (GRU) networks optimized by an improved model of the NGO (northern goshawk optimization) algorithm. The proposed approach has several advantages over existing methods, including its ability to analyze large and complex datasets, its high accuracy, as well as its capacity to detect oral cancer at the very beginning stage. The improved NGO algorithm is utilized to improve the GRU network that helps to improve the performance of the network and increase the accuracy of the diagnosis. The paper describes the proposed approach and evaluates its performance using a dataset of oral cancer patients. The findings of the study demonstrate the efficiency of the suggested approach in accurately diagnosing oral cancer.

关键词： Oral cancer Diagnosis Gated recurrent unit networks northern goshawk optimization algorithm Recurrent neural networks Medical imaging Deep learning Machine learning Artificial intelligence

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Vibration trend forecasting of motorized spindle on the basis of signal processing and deep learning

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NONLINEAR DYNAMICS 2025年第14期113卷 17477-17490页

作者： Dai, Ye Liu, Xiao Pang, Jian Li, Yining Lv, Yanjun Niu, Yongning Harbin Univ Sci & Technol Key Lab Adv Mfg Intelligent Technol Minist Educ Harbin 150080 Peoples R China Harbin Univ Sci & Technol Inst Digital Design & Automat Machinery Prod Dev Harbin 150080 Peoples R China

Currently, the diagnosis of motorized spindle faults primarily focuses on analyzing the current vibration patterns but often neglects forecasting future vibration tendencies. This study introduces a novel vibration trend prediction model for high-speed motorized spindles to anticipate and mitigate potential vibration issues. Acknowledging the noise challenges inherent in data acquisition, this research integrates variational modal decomposition with LMS adaptive filtering and employs the state-of-the-art northern goshawk optimization algorithm for enhancement. The efficiency of this signal processing technique is experimentally validated. In terms of predicting vibration patterns, long short-term memory network architecture is utilized. Moreover, the prediction model is further optimized using the northern goshawk optimization algorithm. Comparative experiments confirm that this optimized model surpasses the standard LSTM model in accurately predicting the future vibration trends of high-speed motorized spindles. By interpreting these predictive results, it enables the proactive determination of the spindle's vibration trend, thereby aligning with the requirements of predictive maintenance strategies.

关键词： Motorized spindle northern goshawk optimization algorithm Signal processing Long short-term memory network Vibration trend prediction

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A novel wind power deterministic and interval prediction framework based on the critic weight method, improved northern goshawk optimization, and kernel density estimation

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RENEWABLE ENERGY 2024年 226卷

作者： Hou, Guolian Wang, Junjie Fan, Yuzhen Zhang, Jianhua Huang, Congzhi North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China

Wind power prediction holds significant importance for the stable operation of power systems and the enhancement of energy utilization efficiency. Interval prediction, capable of providing more uncertain information, has garnered widespread attention. In this study we have designed a hybrid prediction framework for both deterministic and interval prediction. Initially, the original wind power data undergo cleaning using the quartile method and the fuzzy C-means (FCM) clustering algorithm. This approach not only eliminates scattered outliers but also identifies concentrated outliers more effectively. Subsequently, five distinct single prediction models were employed for point predictions. The critic weight method was applied to yield point forecasting results with higher prediction accuracy. Nonparametric kernel density estimation (KDE) and normal distribution (ND) were incorporated to calculate the prediction interval (PI) under varying confidence levels. Finally, an improved northern goshawk optimization (INGO) algorithm was proposed, integrating three enhancement methods: levy flight, sinusoidal mapping, and a reverse learning strategy. This optimization aims to fine-tune the weight coefficients of the aforementioned two interval prediction methods. The resulting PI overcomes the limitations of a single-interval prediction model. To effectively demonstrate the forecasting performance of the proposed prediction framework, two datasets from a wind turbine in northwest China were selected for simulation verification. In deterministic prediction, the designed hybrid forecast model demonstrates a reduction in root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) by 8.5%, 16.98%, and 11.49%, respectively, compared to other single prediction models. In interval prediction, the prediction interval coverage probability (PICP) is enhanced, while the prediction interval normalized average width (PINAW) is effectively reduced. The

关键词： Wind power prediction Fuzzy C-means clustering Quartile method Kernel density function northern goshawk optimization algorithm Interval prediction

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Brain tumor classification utilizing Triple Memristor Hopfield Neural Network optimized with northern goshawk optimization for MRI image

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年第PartA期95卷

作者： Jaga, Satyavati Devi, K. Rama Chaitanya Bharati Inst Technol A Dept Elect & Commun Engn Hyderabad India JNTU Coll Engn Dept Elect & Commun Engn Kakinada 533003 Andhra Pradesh India

Brain tumor classification plays a significant role in exact detection of abnormal brain tissues and facilitates the clinical diagnosis of patients. The computer vision researchers have created numerous algorithms, but they still suffer from low accuracy. Therefore, a Brain Tumor Classification utilizing Triple Memristor Hopfield Neural Network optimized with northern goshawk optimization is proposed in this paper for MRI Image (BTC-TMHNNNGOA). Here, the input imageries are pre-processed to increase the images quality using Median Modified Weiner Filtering (MMWF) method. The pre-processed image is supplied to Synchro Extracting Chirplet Transform (SECT) to extract the features of anti-noise interference capability and image resolution. Then the Triple Memristor Hopfield Neural Network (TMHNN) for classifying Tumor and Non-Tumor Image. Afterward, northern goshawk optimization algorithm (NGOA) is used to optimize the weight parameters of TMHNN classifier for precise classification. The proposed BTC-TMHNN-NGOA technique is activated in MATLAB under metrics, like precision, accuracy, sensitivity, specificity, ROC, F1-score, computational time and computation cost. The proposed method attains 13.88%, 8.75%, and 8.46% higher accuracy for tumor;9.47%, 14.51% and 10.23% higher accuracy for Non-Tumor on MRI brain image dataset compared with existing methods like Automated Brain Tumor Categorization utilizing Optimized Hybrid Neural Network (EABTC-OHNN), Convolutional Neural Network for MRI-Based Brain Tumor Categorization (CNN-MRI-BTC), Enhancing Convolutional Neural Network using Hybridized Elephant Herding optimization approach for MRI Categorization Glioma Brain Tumor Grade (OCNN-HEHOA-MRIC-GBTG). The comparative results exemplify the effectiveness of the proposed method and underscore its advantages in automating brain tumor classification.

关键词： Median Modified Weiner Filtering northern goshawk optimization algorithm Synchro extracting Chirplet transform Triple Memristor Hopfield Neural Network

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In-field calibration of triaxial accelerometer based on PE-ANGO

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TM-TECHNISCHES MESSEN 2024年第2期91卷 86-101页

作者： Qiao, Meiying Yao, Wenhao Gao, Kefei Du, Heng Zhao, Kaidong Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo 454003 Peoples R China

This paper presents a new method for in-field calibration of accelerometers to address the problems of low efficiency and high cost associated with traditional calibration methods. A nonlinear mathematical model of the accelerometer is established, and the cost function is analysed and deduced the cost function. Then, an adaptive northern goshawk Optimisation (NGO) algorithm based on prior knowledge enhancement is introduced. A method of collecting multi-position data with a hand-held accelerometer is introduced, and the proposed algorithm is used to in-field calibrate nine parameters of the accelerometer's nonlinear error model. In addition, simulation is used to compare the results of calibrating the accelerometer using the proposed algorithm and the original algorithm, demonstrating the superiority of the proposed algorithm. Finally, experimental results confirm that the proposed method can rapidly calibrate accelerometer error parameters without relying on complex equipment and with greater accuracy than traditional methods.

关键词： calibration Inertial Measurement Unit three-axis accelerometer intelligent optimization algorithm northern goshawk optimization algorithm

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Study on Traveling Wave Fault Localization of Transmission Line Based on NGO-VMD algorithm

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ENERGIES 2024年第9期17卷 2003页

作者： Yu, Ke Zhu, Xueling Cao, Wensi North China Univ Water Resources & Elect Power Fac Elect Engn Zhengzhou 450045 Peoples R China

To address the challenge of inaccurate fault location of variational mode decomposition (VMD) in practical engineering, due to poor choice of mode decomposition number K and quadratic penalty factor alpha, a traveling wave fault location method using northern goshawk optimization algorithm (NGO) to optimize VMD was proposed. First, the NGO algorithm is used to optimize VMD, and the optimal K and alpha are obtained. Secondly, the optimal parameters are inputted into VMD for fault signal decomposition, and the eigenmode components are obtained. Due to the difficulty of identification of the traveling wave head in the process of traveling wave propagation, Hilbert transform is used to determine the time of initial arrival of the traveling wave head at both ends of the line, and the fault location is precisely calculated by using the two-ended traveling wave fault detection formula. Finally, simulation experiments are carried out to verify the accuracy of the proposed location method, which shows that the proposed location method can locate the fault more accurately and has good engineering application value.

关键词： fault location double-ended traveling wave variational modal decomposition northern goshawk optimization algorithm Hilbert transform

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Anomaly Detection of Transformer Based on NGO and DBSCAN 4

Anomaly Detection of Transformer Based on NGO and DBSCAN

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4th Power System and Green Energy Conference (PSGEC)

作者： Wang, Kai Li, Ke Wang, Quanhu Jia, Jinglong Sun, Tong State Grid Corp China East China Branch Shanghai Peoples R China Shanghai Jiao Tong Univ Key Lab Control Power Transmiss & Convers Minist Educ Shanghai Peoples R China

ISBN: (纸本)9798350365573;9798350365580

The conventional anomaly detection techniques for transformers are inadequate for accurately identifying anomalous states. This paper proposes an anomaly detection method for big data analysis based on NGO (northern goshawk optimization) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). DBSCAN is insensitive to noise and abnormal value, exhibits robust performance, and is capable of automatically adapting to data distribution and complexity. The DBSCAN unsupervised density clustering algorithm is employed for anomaly detection in big data analytic. The DBSCAN is characterized by its insensitivity to noise and outliers, its robustness, and its flexibility, which enables it to adapt to the distribution and complexity of data. The model optimizes the parameters. (radius of the neighborhood) and MinPts (minimum number of points contained in the neighborhood) in DBSCAN using the NGO. Thereafter, the data is clustered by DBSCAN in order to differentiate between normal and abnormal data. The clustering results are combined with the abnormal state determination rules in order to judge that the transformer is in an abnormal state. Upon analyzing the example results express that the NGO-DBSCAN algorithm possesses higher clustering efficiency than the Particle Swarm optimization (PSO) and Scatter Search (SS) optimized DBSCAN. In addition, the algorithm is able to detect anomalous states of the monitoring transformer earlier.

关键词： Transformer Anomaly detection northern goshawk optimization algorithm DBSCAN

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