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improved seagull optimization algorithm of partition and XGBoost of prediction for fuzzy time series forecasting of COVID-19 daily confirmed

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ADVANCES IN ENGINEERING SOFTWARE 2022年 173卷 103212页

作者： Xian, Sidong Chen, Kaiyuan Cheng, Yue Chongqing Univ Posts & Telecommun Key Lab Intelligent Anal & Decis Complex Syst Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing 400065 Peoples R China

The establishment of fuzzy relations and the fuzzification of time series are the top priorities of the model for predicting fuzzy time series. A lot of literature studied these two aspects to ameliorate the capability of the forecasting model. In this paper, we proposed a new method(FTSOAX) to forecast fuzzy time series derived from the improved seagull optimization algorithm(ISOA) and XGBoost. For increasing the accurateness of the forecasting model in fuzzy time series, ISOA is applied to partition the domain of discourse to get more suitable intervals. We improved the seagull optimization algorithm(SOA) with the help of the Powell algorithm and a random curve action to make SOA have better convergence ability. Using XGBoost to forecast the change of fuzzy membership in order to overcome the disadvantage that fuzzy relation leads to low accuracy. We obtained daily confirmed COVID-19 cases in 7 countries as a dataset to demonstrate the performance of FTSOAX. The results show that FTSOAX is superior to other fuzzy forecasting models in the application of prediction of COVID-19 daily confirmed cases.

关键词： improved seagull optimization algorithm Fuzzy time series XGBoost COVID-19

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Fault Diagnosis of Oil-immersed Transformer Based on improved seagull optimization algorithm to Optimize Wavelet Neural Network 9

Fault Diagnosis of Oil-immersed Transformer Based on Improve...

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9th International Forum on Electrical Engineering and Automation (IFEEA)

作者： Wang, Jingou Shan, Yafeng Fu, Hua Liaoning Tech Univ Fac Elect & Control Engn Huludao Liaoning Peoples R China

ISBN: (纸本)9781665464215

Aiming at the problems that the fault diagnosis accuracy of oil-immersed transformers in power systems is low and the diagnosis results are difficult to cover the entire transformer. A new fault diagnosis algorithm is proposed. ISOA is used to improve the extraction effect of fault data features, and WNN algorithm is used to realize the fault information classification and prediction. The fault diagnosis application results show that. The performance test results of the two algorithms are excellent, and the correct rate of fault information feature extraction is 94.65%, which is better than other algorithms. Predict and analyze the failure to reduce the difficulty of maintenance by technicians. The research content will effectively solve problems such as fault diagnosis of oil-immersed transformers, and has great value for the development of power systems.

关键词： Oil-immersed transformer fault diagnosis improved seagull optimization algorithm wavelet neural network

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Smart City Management based Data applications using improved seagull optimization algorithm with Dense Net 121

Smart City Management based Data applications using Improved...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Liu, Tong Lu, Haifei Shenyang Jianzhu Unversity Shenyang China Zhejiang Shuren University College of Urban Construction Hangzhou China

ISBN: (纸本)9798350375442

Recently, rapid growth of information technology, has progressively entered the daily life of combinations. Smart city management is the innovative technology by combining Internet of Things (IoT), cloud computing and mobile Internet technologies to achieve modern living experience. By analyzing the current situation of smart city this research presented improved seagull optimization algorithm with Dense Net -121 (ISOA-Dense Net-121) to improve processes in digitalization economy. The suggested model involves sensor-integrated systems to optimize by generating accurate predictions. ISOA is utilized to simulate the seagull migration for global exploration, resulting in better weight initialization. Then, DenseNet-121 utilized to improve the performance in difficult image recognition tasks. By removing the unnecessary data applications, these hybridized model ISOA-DenseNet-121 reduce the environmental effects in SWM. According to the result it illustrated that when compared to existing models such as ResNeXt, SWM and Smart Waste Bin Management System (SWBMS) the proposed ISOA-Dense Net-121 recommended strategy outperformed the others in terms of accuracy, f-score of 99.3%, precision of 99.4% and recall of 99.2%. © 2024 IEEE.

关键词： and smart city management cloud computing data applications hybridization models improved seagull optimization algorithm

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Electric Power Load Forecasting Method Based on a Support Vector Machine Optimized by the improved seagull optimization algorithm

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ENERGIES 2022年第23期15卷 9197页

作者： Zhang, Suqi Zhang, Ningjing Zhang, Ziqi Chen, Ying Tianjin Univ Commerce Sch Informat Engn Tianjin 300134 Peoples R China Tianjin Univ Commerce Sch Sci Tianjin 300134 Peoples R China South China Co China Construct Engn Bur Ltd Shenzhen 518000 Peoples R China State Grid Tianjin Mkt Serv Ctr Metrol Ctr Tianjin 300200 Peoples R China

Accurate load forecasting is conducive to the formulation of the power generation plan, lays the foundation for the formulation of quotation, and provides the basis for the power management system and distribution management system. This study aims to propose a high precision load forecasting method. The power load forecasting model, based on the improved seagull optimization algorithm, which optimizes SVM (ISOA-SVM), is constructed. First, aiming at the problem that the random selection of internal parameters of SVM will affect its performance, the improved seagull optimization algorithm (ISOA) is used to optimize its parameters. Second, to solve the slow convergence speed of the seagull optimization algorithm (SOA), three strategies are proposed to improve the optimization performance and convergence accuracy of SOA, and an ISOA algorithm with better optimization performance and higher convergence accuracy is proposed. Finally, the load forecasting model based on ISOA-SVM is established by using the Mean Square Error (MSE) as the objective function. Through the example analysis, the prediction performance of the ISOA-SVM is better than the comparison models and has good prediction accuracy and effectiveness. The more accurate load forecasting can provide guidance for power generation and power consumption planning of the power system.

关键词： power load forecasting electric management system Support Vector Machine improved seagull optimization algorithm seagull optimization algorithm

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An improved seagull optimization algorithm for optimal coordination of distance and directional over-current relays

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期200卷 1页

作者： Abdelhamid, Mohamed Houssein, Essam H. Mahdy, Mohamed A. Selim, Ali Kamel, Salah Aswan Univ Fac Engn Elect Engn Dept Aswan Egypt Minia Univ Fac Comp & Informat Al Minya Egypt Beni Suef Univ Fac Comp & Artificial Intelligence Bani Suwayf Egypt

In this paper, an improved bio-inspired optimization algorithm called seagull optimization algorithm (ISOA) is proposed. ISOA is developed based on three techniques, these techniques use the collaborative optimization strategy, simulating new bird's generation, and rearranging subgroups. The CEC2020 is utilized to obtain the role and importance of the improved algorithm. Furthermore, the ISOA is applied to find the optimal coordi-nation of Distance and Directional Over-Current Relays (D&DOCRs). The problem of D&DOCRs coordination is more complicated and has numerous constraints. Hence, in this work, a developed object function (DOF) is proposed to set the Distance relays with minimum available settings. ISOA and DOF are tested on the IEEE 8-bus and IEEE 14-bus test systems. ISOA algorithm is used to coordinate the D&DOCRs in both the near and far ends of transmission lines. The results of test systems prove the effectiveness of ISOA to solve the coordination of D&DOCRs using the DOF.

关键词： seagull optimization algorithm improved seagull optimization algorithm Optimal relay coordination Distance relay Directional over-current relay

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ISOA-DBN: A New Data-Driven Method for Studying the Operating Characteristics of Air Conditioners

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ENERGY SCIENCE & ENGINEERING 2025年第1期13卷 160-175页

作者： Zhou, Mengran Zhang, Qiqi Hu, Feng Wang, Ling Zhou, Guangyao Kong, Weile Wu, Changzhen Cui, Enhan Anhui Univ Sci & Technol Sch Elect & Informat Engn Huainan Peoples R China Anhui Univ Sci & Technol State Key Lab Min Response & Disaster Prevent & Co Huainan Peoples R China

Air conditioning load is a crucial demand response resource for optimizing energy consumption control, and its accurate analysis provides an essential basis for achieving efficient energy management. We aim at solving the problems of scarcity, single type, low accuracy and difficult construction of high-quality data sets available for air conditioning operation characteristic models at present. This paper proposes a construction method of air conditioning operation characteristic model based on an improved seagull optimization algorithm to optimize deep belief network (ISOA-DBN). Firstly, the data set for the study of air conditioning operation characteristics is obtained through experiments. Secondly, the Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN) are used to study the operating characteristics of air conditioning. The results show that the model effect is better when DBN is used to study the operating characteristics of air conditioning, and the coefficient of determination reaches 0.9439. Then, the SOA is improved, and its performance is tested. The results show that ISOA performs better than SOA in the test of 14 standard functions. Finally, the ISOA is used to adjust the DBN parameters finely. The results show that compared with DBN and SOA-DBN, ISOA-DBN has a better model effect when used to study the operating characteristics of air conditioners, and the coefficient of determination reaches 0.9534. This can provide strong support for studying air conditioning operating characteristics under different working conditions and has broad application prospects in optimizing energy consumption control.

关键词： air conditioning operation characteristics deep belief network improved seagull optimization algorithm restricted Boltzmann machine

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A neural network-based prediction model in water monitoring networks

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WATER SUPPLY 2021年第5期21卷 2347-2356页

作者： Ji, Xiaohong Pan, Ying Jia, Guoqing Fang, Weidong Qinghai Nationalities Univ Xining 810007 Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Sci & Technol Microsyst Lab Shanghai 201800 Peoples R China

To improve the prediction accuracy of ammonia nitrogen in water monitoring networks, the combination of a bio-inspired algorithm and back propagation neural network (BPNN) has often been deployed. However, due to the limitations of the bio-inspired algorithm, it would also fall into the local optimal. In this paper, the seagull optimization algorithm (SOA) was used to optimize the structure of BPNN to obtain a better prediction model. Then, an improved SOA (ISOA) was proposed, and the common functional validation method was used to verify its optimization performance. Finally, the ISOA was applied to improve BPNN, which is known as the improved seagull optimization algorithm-back propagation (ISOA-BP) model. The simulation results showed that the prediction accuracy of ammonia nitrogen was greatly improved and the proposed model can be better applied to the prediction of complex water quality parameters in water monitoring networks.

关键词： ammonia nitrogen prediction back propagation neural network improved seagull optimization algorithm neural network seagull optimization algorithm water monitoring

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Brain tumor diagnosis based on metaheuristics and deep learning

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2021年第2期31卷 657-669页

作者： Hu, An Razmjooy, Navid PLA Navy Anqing Hosp Neurosurg Anqing 246003 Anhui Peoples R China Tafresh Univ Dept Elect Engn Tafresh Iran

The high mortality rate associated with brain tumors requires early detection in the early stages to treat and reduce mortality. Due to the complexity of brain tissue, manual diagnosis of the brain and tumor tissues is very time-consuming and operator dependent. Furthermore, there is a need for experts who can review the images to detect these effects, rendering traditional methods inefficient in their presence. Therefore, the use of automated procedures for the careful examination of tumors can prove useful. In this study, a new metaheuristic-based system is presented for the early detection of brain tumors. The proposed method implements three main steps, namely tumor segmentation, feature extraction, and classification based on a deep belief network. An improved version of the seagull optimization algorithm is adopted for optimal selection of the features and classification of the images. The simulation results of the proposed method are compared with a few existing methods. The final results demonstrate that the proposed method exhibits superior performance in terms of the CDR, FAR, and FRR indices compared with the other methods.

关键词： brain tumor deep belief network feature extraction feature selection classification improved seagull optimization algorithm

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Research on Joint Distribution Path Planning of Electric Logistics Vehicles with Different Recharge Modes

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TRANSPORTATION RESEARCH RECORD 2024年

作者： Wang, Jing Gao, Tingting Zhang, Jingzhe Tu, Chuang Yanshan Univ Sch Econ & Management Qinhuangdao Hebei Peoples R China Yanshan Univ Res Ctr Reg Econ Dev Qinhuangdao Hebei Peoples R China

To address the route planning issues under the community group purchase model for joint delivery, this study thoroughly considers electric logistics vehicles with different recharging methods. The objective is to minimize the sum of operating costs, recharging costs, time window penalty costs, and carbon emission costs. Separate multi-objective optimization models for route planning are constructed for both charging and battery-swapping logistics vehicles. An improved seagull optimization algorithm, guided by the golden sine strategy of the L & eacute;vy flight guidance mechanism, is employed to avoid local optima and enhance the solution efficiency. The feasibility of the models and the algorithm is verified through simulation examples. Experimental results show that, at the current stage, battery-swapping logistics vehicles display significant advantages over charging electric logistics vehicles. Although battery-swapping logistics vehicles extend delivery time, they can reduce the total delivery costs to a certain extent. Therefore, the future development prospects of battery-swapping logistics vehicles will be even broader.

关键词： joint delivery urban distribution electric logistics vehicles route planning improved seagull optimization algorithm

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