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Research on the seagull optimization algorithm-based convolutional neural network rolling bearing fault diagnosis method

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ENGINEERING RESEARCH EXPRESS 2023年第3期5卷 035050-035050页

作者： Xue, Jijun Liu, Xiaodong Xu, Hao Zhang, Di Xian Shiyou Univ Mech Engn Coll Xian 710065 Shaanxi Peoples R China

It is crucial to understand the rolling bearing fault diagnosis procedure since rolling bearings are frequently used in rotating machinery and if a failure occurs, it will interfere with the proper operation of the entire piece of machinery or piece of equipment. Deep learning is increasingly being used in mechanical fault diagnosis, with convolutional neural networks(CNN) being the most common type. In recent years, the rapid growth of artificial intelligence has caused fault diagnosis methods to evolve as well. A CNN fault diagnostic approach based on seagull optimization algorithm (SOA) is suggested and applied to the fault diagnosis of rolling bearings in order to address the issues with convolutional neural networks, such as high data requirements, unstable gradients, and optimization challenges. The seagull optimization approach is used to design a deep learning model by selecting the structural hyperparameters in the CNN model as efficiently as possible. The accelerated life experimental dataset for rolling bearings is trained using the optimized CNN model. The training outcomes of the CNN model on the dataset before and after optimization are compared, and the findings demonstrate that the suggested method has a superior optimization effect. As can be seen, the seagull optimization algorithm and CNN can be organically combined and applied to the fault diagnosis method for rolling bearings.

关键词： rolling bearing fault diagnosis convolutional neural network seagull optimization algorithm

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Improved seagull optimization algorithm Based on Multi-Strategy Integration

Improved Seagull Optimization Algorithm Based on Multi-Strat...

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第34届中国控制与决策会议

作者： Haibin Shi Baoda Li College of Information Science and Engineering Northeastern University

Aiming at the defects of seagull optimization algorithm(SOA) in solving optimization problems,such as local optimization,slow convergence speed and low optimization accuracy,a seagull optimization algorithm(SCSOA) based on integration of improved Sobol sequence and Cauchy variation is ***,initialize the population using the Sobol sequence to make the seagulls more evenly distributed in the initial solution space;Secondly,use a nonlinear function to replace the original convergence factor to guide the seagull to always maintain a larger body degree of freedom during the search process,enhance the global search ability,and avoid falling into the local optimum;Then,the Cauchy variation strategy is introduced,so that the individual can better find the optimal solution and enhance the ability of the algorithm to jump out of the local optimum;Finally,use the benchmark function to test the improved algorithm,and compare it with the original algorithm and the experimental results of other *** results show that SCSOA performs better in convergence speed and optimization accuracy,and the global optimization capability is also improved.

关键词： seagull optimization algorithm Sobol Sequence Cauchy Variation

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Short term wind speed prediction based on CEESMDAN and improved seagull optimization kernel extreme learning machine

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EARTH SCIENCE INFORMATICS 2025年第1期18卷 1-33页

作者： Qin, Xiwen Yuan, Liping Dong, Xiaogang Zhang, Siqi Shi, Hongyu Changchun Univ Technol Sch Math & Stat Changchun 130012 Peoples R China Jilin Prov Key Lab Data Sci & Intelligent Decis Changchun 130012 Peoples R China

Accurate wind speed predictions are crucial for the planning, operation, and energy management of wind farms. In this paper, we propose a novel wind speed prediction model, CEESMDAN-LNR-SOA-KELM. Firstly, we employ the CEESMDAN decomposition method to extract features from the original wind speed data, capturing the underlying characteristics of the data. Secondly, we apply a nonlinear treatment to the convergence factor A of the seagull optimization algorithm (SOA) to better adapt to the complexity and diversity of the problem, thereby enhancing the algorithm's convergence speed. Additionally, we introduce a random opposition-based learning strategy to effectively prevent the SOA algorithm from getting stuck in local optima. We further optimize the parameters of KELM using LNR-SOA. The results of function optimization demonstrate that the proposed improvement strategy significantly enhances the parameter optimization capability of the SOA algorithm. The wind speed data from the Sotavento Galicia wind farm in Spain were used as the subject of the numerical experiments. The experimental results indicate that the model proposed in this paper demonstrates higher accuracy and reliability in wind speed prediction compared to the comparative models. It provides an effective forecasting tool for the wind energy industry and meteorological predictions.

关键词： Wind speed prediction Adaptive decomposition Random opposition-based learning seagull optimization algorithm Kernel extreme learning machine

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optimization of WSN localization algorithm based on improved multi-strategy seagull algorithm

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TELECOMMUNICATION SYSTEMS 2024年第3期86卷 547-558页

作者： Yu, Xiuwu Liu, Yinhao Liu, Yong Univ South China Hengyang 421000 Hunan Peoples R China Shenzhen Univ Shenzhen 518000 Peoples R China

This paper proposes a multi-strategy modified seagull algorithm to optimize DV-Hop localization algorithm (DISO) to improve the precision of non-range-ranging localization algorithm in wireless sensor networks. Firstly, the algorithm analyzes the causes of errors in the positioning of the traditional non-ranging location algorithm DV-Hop, and improves these steps. Among them, the communication area of anchor nodes is divided by different radii, so as to reduce the influence of distance on hop number. The node distribution is stochastic, so the mean square error is used instead of the unbiased estimation, and the weight is introduced to calculate the average jump distance, which reduces the error caused by the random distribution of nodes. Secondly, the objective function optimization method is used to replace the trilateral measurement, and the improved seagull optimization algorithm is used for iterative optimization. Finally, the seagull optimization algorithm is modified in view of its shortcomings. The chaotic mapping was used to initialize the seagull population and increase its diversity. The flight parameters of seagull and the position update methods of the worst and best seagull are improved, and the optimization ability of the algorithm is improved by combining levy flight mechanism and T distribution variation strategy. The simulation results show that the initial population distribution of DISO algorithm is more uniform, which establishes a basic advantage for the subsequent optimization. Keeping the other parameters consistent, DISO algorithm has higher positioning accuracy than other comparison algorithms, no matter changing the number of anchor nodes or the total number of nodes or changing the communication radius. The positioning errors of DISO algorithm are reduced by 45.63%, 17.17%, 22.61% and 11.68% compared with DV-Hop algorithm and other comparison algorithms under different number of anchor nodes. Under different total number of nodes, the pos

关键词： Wireless sensor network Range-free location seagull optimization algorithm Chaotic mapping

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seagull-Cuckoo Search algorithm for Function optimization 6

Seagull-Cuckoo Search Algorithm for Function Optimization

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6th International Conference for Convergence in Technology (I2CT)

作者： Das, Gyanesh Panda, Rutuparna VSSUT Dept ECE Burla 768018 Odisha India

ISBN: (纸本)9781728188768

This paper anticipates to frame a hybrid seagull optimization algorithm - Cuckoo Search (SOA-CS) algorithm for objective function minimization. The migration and attacking strategies of seagulls are complimented by the social breeding behaviour of cuckoo birds. In addition, the thought of Levy flight walking methodology is also inherited. Twenty-three standard benchmark test functions are used for validation. To figure out the outcomes - the proposed algorithm converges faster and provides better results over both the SOA and CS algorithms.

关键词： seagull optimization algorithm Cuckoo Search Levy Flight

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Multi-Role collaborative framework for structural damage identification considering measurement noise effect

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MEASUREMENT 2025年 250卷

作者： Chen, Zepeng Zhang, Zhiyu Chen, Xiangmei Hou, Rongrong Ding, Zhenghao Liu, Feng Yang, Zhicheng Foshan Univ Sch Civil Engn & Transportat Foshan Peoples R China Harbin Inst Technol Sch Civil Engn Harbin Peoples R China Kyoto Univ Dept Civil & Earth Resources Engn Kyoto Japan Guangdong Univ Technol Sch Civil & Transportat Engn Guangzhou Peoples R China Zhongkai Univ Agr & Engn Coll Urban & Rural Construct Guangzhou Peoples R China

Swarm intelligence has been extensively applied in structural damage identification, but a single method may not perform well in identification, especially using limited and noised vibration data. In this context, the objective landscape of the formulated identification problem is often ill-posed, indicating the optimized landscape is filled with many local optimal points. If the algorithm gets trapped in local optimal points, it will not obtain satisfactory identification results. To address this issue, this study introduces the sparse regularization technique to construct a well-posed objective function. Furthermore, a novel multi-role collaborative framework is proposed, which integrates different swarm intelligent and enables the individual in the algorithm to switch different roles, meaning employing different updating strategies, for the demands of different identification cases. Therefore, a more accurate identification results can be obtained. A series of numerical simulations and a laboratory validation on a box-section beam with multiple notches are carried out. The features of multi-role adaptive mechanism and diversity search strategies in the proposed framework guarantee its advantages and superiority on obtaining better identifications compared with single swarm intelligence algorithm, providing a new way in developing high-efficiency model updating and damage detection algorithms.

关键词： Multi-role collaborative framework seagull optimization algorithm Sine cosine algorithm Sparse regularization Noise effect

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SGA-Driven feature selection and random forest classification for enhanced breast cancer diagnosis: A comparative study

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SCIENTIFIC REPORTS 2025年第1期15卷 1-23页

作者： Yaqoob, Abrar Verma, Navneet Kumar Mir, Mushtaq Ahmad Tejani, Ghanshyam G. Eisa, Nashwa Hassan Babiker Osman, Hind Mamoun Hussien Shah, Mohd Asif VIT Bhopal Univ Sch Adv Sci & Language Bhopal 466114 India Yuan Ze Univ Dept Ind Engn & Management Taoyuan 320315 Taiwan Saveetha Univ Saveetha Dent Coll & Hosp Saveetha Inst Med & Tech Sci Dept Res Analyt Chennai 600077 India King Khalid Univ Coll Appl Med Sci Dept Clin Lab Sci Abha 61421 Saudi Arabia Kardan Univ Dept Econ Parwane Du Kabul 1001 Afghanistan Lovely Profess Univ Div Res & Dev Phagwara 144001 Punjab India

In this study, we propose a novel approach for breast cancer classification that integrates the seagull optimization algorithm (SGA) for feature selection with the Random Forest (RF) classifier for effective data classification. The novelty of our approach lies in the first-time application of SGA for gene selection in breast cancer diagnosis, where SGA systematically explores the feature space to identify the most informative gene subsets, thereby improving classification accuracy and reducing computational complexity. The selected features are subsequently classified using RF, known for its robustness and high accuracy in handling complex datasets. To evaluate the effectiveness of the proposed method, we compared it with other classifiers, including Linear Regression (LR), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). The proposed SGA-RF combination achieved a best mean accuracy of 99.01% with 22 genes, outperforming other methods and demonstrating consistent performance across varying feature subsets. The mean accuracies ranged from 85.35 to 94.33%, highlighting a balance between feature reduction and classification accuracy. Future work will explore the integration of other nature-inspired algorithms and deep learning models to further enhance performance and clinical applicability.

关键词： seagull optimization algorithm Random forest Cancer classification High dimensional data

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A novel hybrid ESO-DE-WHO algorithm for solving real-engineering optimization problems

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2025年第1期16卷 254-309页

作者： Panigrahy, Damodar Samal, Padarbinda SRM Inst Sci & Technol Coll Engn & Technol Dept Elect & Commun Engn Chennai 603202 Tamilnadu India KIIT Deemed Be Univ Sch Elect Engn Bhubaneswar India

The proposed algorithm is based on the combination of enhanced seagull optimization (ESO) algorithm, differential evolution (DE) algorithm, wild horse optimization (WHO) algorithm with probability matrix to solve optimization problems. The seagull optimized algorithm is enhanced to avoid premature convergence. A probabilistic matrix with an equal value of each location is generated having 3 columns and the number of populations as rows. The three columns represent enhanced seagull optimization algorithm, differential evolution algorithm, and wild horse optimization algorithm respectively. Initial decision vector is generated in the bound. The probabilistic matrix after each iteration is updated with respect to minimum fitness value among 3 algorithms for each population. The decision vector for the next stage is decided based on the highest value of probability matrix. This stage improves the learning process and reduces the convergence rate. The updation of the probabilistic matrix and selection of decision vector for the next stage is continued until certain criteria are achieved. The optimized result of the proposed algorithm is the final minimum value. The proposed methodology is validated by comparing mean, standard deviation, best and worst value with ten other algorithms for 95 functions of CEC-2021. These algorithms are Archimedes optimization, wild horse optimizer, cuckoo search, differential evolution, black widow optimization, seagull optimization, whale optimization, arithmetic optimization, coot optimization, enterprise development (ED), hybrid jellyfish search and particle swarm optimization (HJPSO), modified lightning search algorithm (MLSA), polar lights optimizer (PLO), and parrot optimizer algorithm. The efficiency of the proposed methodology was also validated statistically with the ten methodologies The proposed methodology is also verified by five real-time engineering optimization problems. The results show better performance of the proposed al

关键词： CEC-2021 Differential evolution algorithm seagull optimization algorithm Wild horse optimizer algorithm

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Method for Predicting Ultra-Short-Term Wind Speed and Direction at Emergency Well Site

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH 2025年第1期19卷 1-15页

作者： Pan, Zhao Liang, Haibo Li, Jinyun Guangan Vocat & Tech Coll Guangan 638500 Sichuan Peoples R China Southwest Petr Univ Chengdu 610500 Sichuan Peoples R China CNPC Chuanqing Drilling Engn Co LTD Chengdu 610500 Sichuan Peoples R China

In order to protect the safety of emergency rescue personnel and improve the speed of well control emergency rescue, it is necessary to know the ultra-short-term wind speed and direction time series of the well field in the next 15 min in advance. In this paper, the 0-1 test method is used to calculate the chaotic properties of the ultra-short-term wind speed and direction in the well field, and then the Hurst index is used to calculate the predictability of the ultra-short-term wind speed and direction in the well field. The seagull optimization algorithm (SOA) is used to optimize the four hyper-parameters of the Echo State Network (ESN) and to train the network output weight matrix, so that the ultra-short-term wind speed and direction in the well field can be predicted from chaotic time series of the three kinds of terrain of the well field at different time scales. Among the three terrains, the MSE of the ultra-short-term wind speed in the well field valley and the ultra-short-term wind direction in the well field mountain are the smallest, which are 0.0215 and 1.787, respectively. The experimental results show that the SOA-ESN method has a good prediction effect on the chaotic time series of the ultra-short-term wind speed and direction, and can provide a new technical support for speeding up the well control emergency rescue. The ultra-short-term wind speed and direction at well site are chaotic time series and *** proposed SOA-ESN has a good effect on the prediction of ultra-short-term wind speed and direction time series at well *** work provides a new technical support to speed up the emergency rescue of the well control.

关键词： Well control emergency rescue Ultra-short-term wind speed and direction in the well field Echo state network seagull optimization algorithm Chaotic time series

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RUL Prediction Method for Lithium-Ion Batteries Based on the SOA-ELM algorithm

ENGINEERING REPORTS

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ENGINEERING REPORTS 2025年第3期7卷

作者： Meng, Xiangdong Zhang, Haifeng Li, Dexin Dong, Yunchang Zhang, Jiajun Cao, Xinyu Li, Gang Jilin Elect Power Co Ltd State Grid Elect Power Res Inst Changchun Peoples R China Jilin Univ Coll Instrumentat & Elect Engn Changchun Peoples R China

With the rapid advancement of electrochemical energy storage power stations and electric vehicles, lithium-ion batteries have gained widespread adoption due to their high specific energy and superior power performance. However, the increasing frequency of safety incidents in electrochemical energy storage facilities in recent years has raised significant concerns. Effective monitoring of lithium-ion battery conditions is crucial to ensure the safety of power systems and support the sustainable growth of the electrochemical energy storage industry. Among the key challenges, accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is essential for maintaining the safe and reliable operation of battery management systems. This paper proposes an advanced RUL prediction model that combines the seagull optimization algorithm (SOA) with the extreme learning machine (ELM) to enhance prediction accuracy. The proposed SOA-ELM model is validated using the NASA dataset, and the results demonstrate its effectiveness and potential in improving RUL prediction for lithium-ion batteries. This study contributes to the development of more reliable and efficient battery management systems, paving the way for safer and more sustainable energy storage solutions.

关键词： extreme learning machine lithium-ion batteries RUL seagull optimization algorithm

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