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Improved multi-strategy adaptive grey wolf optimization for practical engineering applications and high-dimensional problem solving

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第10期57卷 277页

作者： Yu, Mingyang Xu, Jing Liang, Weiyun Qiu, Yu Bao, Sixu Tang, Lin Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China New York Inst Technol Dept Biol & Chem Sci New York NY 11568 USA

The grey wolf optimization (GWO) is a highly effective meta-heuristic algorithm leveraging swarm intelligence to tackle real-world optimization problems. However, when confronted with large-scale problems, GWO encounters hurdles in convergence speed and problem-solving capabilities. To address this, we propose an Improved Adaptive grey wolf optimization (IAGWO), which significantly enhances exploration of the search space through refined search mechanisms and adaptive strategy. Primarily, we introduce the incorporation of velocity and the Inverse Multiquadratic Function (IMF) into the search mechanism. This integration not only accelerates convergence speed but also maintains accuracy. Secondly, we implement an adaptive strategy for population updates, enhancing the algorithm's search and optimization capabilities dynamically. The efficacy of our proposed IAGWO is demonstrated through comparative experiments conducted on benchmark test sets, including CEC 2017, CEC 2020, CEC 2022, and CEC 2013 large-scale global optimization suites. At CEC2017, CEC 2020 (10/20 dimensions), CEC 2022 (10/20 dimensions), and CEC 2013, respectively, it outperformed other comparative algorithms by 88.2%, 91.5%, 85.4%, 96.2%, 97.4%, and 97.2%. Results affirm that our algorithm surpasses state-of-the-art approaches in addressing large-scale problems. Moreover, we showcase the broad application potential of the algorithm by successfully solving 19 real-world engineering challenges.

关键词： grey wolf optimization algorithm Search mechanism Inverse multiquadratic function Adaptive strategy Engineering design challenges

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Optimal operation of new coastal power systems with seawater desalination based on grey wolf optimization

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ENERGY REPORTS 2023年 9卷 391-402页

作者： Gao, Yujie Yang, Hao Zhou, Bowen Chen, Xinyi Hu, Zhijun Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ Key Lab Integrated Energy Optimizat & Secure Opera Shenyang 110819 Peoples R China Northeastern Univ Sch Resources & Civil Engn Shenyang 110819 Peoples R China

Seawater desalination is one of the effective means to efficiently consume renewable energy and to improve the flexibility of system control in the new power system. In the optimal operation of new power systems, seawater desalination is usually considered as an entirety, and a motor equivalent model is used to simulate the overall external characteristics of seawater desalination. The characteristics of multiple sets of motors with multiple equipment processes inside the seawater desalination are neglected, which makes operation control characteristics inaccurate. In order to solve the above-mentioned problems, this paper firstly studies a seawater desalination system composed of 12 motors, and introduces the specific parameters of these 12 motors in detail. Secondly, after comprehensively considering the constraints of water flow, water pressure, and other factors, three control strategies are proposed for the 12 motors, respectively. Then, a detailed mathematical model of seawater desalination units and an optimal operation model of a new power system with seawater desalination are established. The optimization model aims at the minimum operating cost of seawater desalination, and considers the constraints of practical problems comprehensively such as motor power requirements and water storage capacity. Finally, the grey wolf optimization algorithm (GWO) is used in this paper to solve the optimization problem. The simulation results show that the model proposed in this paper can precisely control each unit in the seawater desalination system and improve the economic benefits of the system. Compared with the Particle Swarm optimization algorithm (PSO) and the Moth-flame optimization algorithm (MFO), the algorithm used in this paper can find the global optimal solution, and has both faster convergence speed and wider practicability. (c) 2023 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/

关键词： Seawater desalination Detail model Optimal operation grey wolf optimization algorithm

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Efficient response analysis of the cable of offshore wind turbine at static state: Hybrid of perturbation method and grey wolf optimization

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OCEAN ENGINEERING 2023年第1期269卷

作者： Cao, Liang Dai, Maolin Zhou, Xuhong Huang, Guoqing Chen, Frank Zhu, Ronghua Hunan Univ Coll Civil Engn Changsha 410082 Peoples R China Hunan Univ Key Lab Damage Diag Engn Struct Hunan Prov Changsha 410082 Peoples R China Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China Penn State Univ Dept Civil Engn Middletown PA 17057 USA Yangjiang Offshore Wind Energy Lab Yangjiang 529500 Guangdong Peoples R China

The mooring cable system is an important component of the offshore floating wind turbine. The mooring system mainly consists of several cables to be used to connect the floating body and seabed. The efficiency of dynamic response analysis of the mooring cable under flow current is generally low and thus complicates the optimal analysis of offshore floating wind turbines. As the determination of the dynamic response of cables highly relies on the static state, the static state response analysis of cables is important. To achieve the static state response analysis of cables, the mooring cable is simplified as a two-point boundary-valued model, which considers the hydrodynamic effect of flow current. Specifically, the perturbation method is first used to linearize the highly nonlinear equations of motion of a cable into the partial differential equations with a constant coefficient for easing the solution of equations. Then, to obtain the undetermined parameters of functions after linearization and reduce the computational cost, the grey wolf optimization algorithm is adopted. Lastly, three examples considering different currents are presented to demonstrate the responses of the mooring cable. Results show that the proposed algorithm can significantly improve the calculation efficiency. To better understand the effects of main parameters on the static response and mooring force of cable, a parametric analysis is carried out under the zero current velocity, considering the cable's stiffness, mass, and diameter as well as the number of nodes. The parametric analysis results indicate that the stiffness influences the response of cables, while the effects from mass and diameter can be ignored.

关键词： Mooring cable grey wolf optimization algorithm Perturbation method Static state Floating wind turbine

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Cloud infrastructure availability optimization using Dragonfly and greywolf optimization algorithms for health systems

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第4期45卷 6209-6227页

作者： Saini, Monika Maan, Vijay Singh Kumar, Ashish Saini, Dinesh Kumar Manipal Univ Jaipur Dept Math & Stat Jaipur 303007 Rajasthan India Manipal Univ Jaipur Dept Comp & Commun Engn Jaipur Rajasthan India

Cloud infrastructure provides a real time computing environment to customers and had wide applicability in healthcare, medical facilities, business, and several other areas. Most of the health data recorded and saved on the cloud. But the cloud infrastructure is configured using several components and that makes it a complex structure. And the high value of availability and reliability is essential for satisfactory operation of such systems. So, the present study is conducted with the prominent objective of assessing the optimum availability of the cloud infrastructure. For this purpose, a novel stochastic model is proposed and optimized using dragonfly algorithm (DA) and grey wolf optimization (GWO) algorithms. The Markovian approach is employed to develop the Chapman-Kolmogorov differential difference equations associate with the system. It is considered that all failure and repair rates are exponentially distributed. The repairs are perfect. The numerical results are derived to highlight the importance of the study and identify the best algorithm. The system attains its optimum availability 0.9998649 at population size 120 with iteration 700 byGWO. It is revealed that grey wolf optimization algorithm performed better than the Dragonfly algorithm in assessing the availability, best fitted parametric values and execution time.

关键词： Availability cloud infrastructure dragonfly algorithm grey wolf optimization algorithm markov process

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grey wolf Accretive Satisfaction algorithm for optimization of Residence Energy Management with Time and Device-based Preferences 8

Grey Wolf Accretive Satisfaction Algorithm for Optimization ...

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8th IEEE International Conference on Power and Energy (PECon)

作者： Ayub, Sara Ayob, Shahrin Bin Md Wei, Tan Chee Aziz, Lubna Balochistan Univ Informat Technol Engn & Manageme FICT Dept Elect Engn Quetta Pakistan Univ Teknol Johar Bahru Fac Engn Sch Elect Engn Dept Elect Engn Johar Bahru Malaysia Balochistan Univ Informat Technol Engn & Manageme FICT Dept Comp Engn Quetta Pakistan

ISBN: (纸本)9781728170688

In residential energy management (REM), time of use (TOU) of appliances scheduling based on user-defined preferences is an essential task performed by the home energy management controller. This paper devised a robust REM technique that capable of monitoring and controlling residential loads within a smart home. The method is based on an improved binary grey wolf accretive satisfaction algorithm (GWASA), which is founded on four hypotheses that allow time-varying preferences to be quantifiable in terms of time and devicedependent features. Based on household appliances TOU, the absolute satisfaction derived from the preferences of appliance and power ratings, the GWASA can produce optimum energy consumption pattern that will give the customer maximum satisfaction at the predefined user budget. A cost per unit satisfaction index is also established to relate daily consumer expenses with the achieved satisfaction. Simulation results on two peak budgets from $1.5/day and $ 2.5/day are carried out to analyze the efficacy of GWASA. Accordingly, the result of each of the scenarios is compared with the result obtained from three other different algorithms, namely, BPSO, BGA, BGWO. The simulation results reveal that the proposed demand side residential management based on GWASA offers the least cost per unit satisfaction and maximum percentage satisfaction in each scenario.

关键词： grey wolf optimization algorithm satisfaction index user comfort energy constraints budget constraints

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IDENTIFICATION AND CONTROL OF A HEAT FLOW SYSTEM BASED ON THE TAKAGI-SUGENO FUZZY MODEL USING THE grey wolf OPTIMIZER

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THERMAL SCIENCE 2022年第3期26卷 2275-2286页

作者： Jovanovic, Radisa Z. Zaric, Vladimir R. Univ Belgrade Fac Mech Engn Belgrade Serbia

Even though, it is mostly used by process control engineers, the temperature control remains an important task for researchers. This paper addressed two separate issues concerning model optimization and control. Firstly, the linear models for the three different operating points of the heat flow system were found. From these identified models a Takagi-Sugeno model is obtained using fixed membership functions in the premises of the rules. According to the chosen objective function, parameters in the premise part of Takagi-Sugeno fuzzy model were optimized using the grey wolf algorithm. Furthermore, by using the parallel distributed compensation a fuzzy controller is developed via the fuzzy blending of three proportional + sum controllers designed for each of the operating points. In order to evaluate performance, a comparison is made between the fuzzy controller and local linear controllers. Moreover, the fuzzy controllers from the optimized and initial Takagi-Sugeno plant models are compared. The experimental results on a heat flow platform are presented to validate efficiency of the proposed method.

关键词： temperature control grey wolf optimization algorithm parallel distributed compensation fuzzy control Takagi-Sugeno discrete-time systems

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Maximum Power Point Tracking Control of Offshore Wind-Photovoltaic Hybrid Power Generation System with Crane-Assisted

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Computer Modeling in Engineering & Sciences 2025年第4期143卷 289-334页

作者： Xiangyang Cao Yaojie Zheng Hanbin Xiao Min Xiao School of Transportation and Logistics Engineering Wuhan University of TechnologyWuhan430063China Sanya Science and Education Innovation Park Wuhan University of TechnologySanya572024China School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhan430063China

This study investigates the Maximum Power Point Tracking(MPPT)control method of offshore windphotovoltaic hybrid power generation system with offshore crane-assisted.A new algorithm of Global Fast Integral Sliding Mode Control(GFISMC)is proposed based on the tip speed ratio method and sliding mode *** algorithm uses fast integral sliding mode surface and fuzzy fast switching control items to ensure that the offshore wind power generation system can track the maximum power point quickly and with low *** offshore wind power generation system model is presented to verify the algorithm *** offshore off-grid wind-solar hybrid power generation systemis built in MATLAB/*** with other MPPT algorithms,this study has specific quantitative improvements in terms of convergence speed,tracking accuracy or computational ***,the improved algorithm is further analyzed and carried out by using Yuankuan Energy’s ModelingTech semi-physical simulation *** results verify the feasibility and effectiveness of the improved algorithm in the offshore wind-solar hybrid power generation system.

关键词： Offshore wind power generation efficiency maximum power point tracking(MPPT) integral sliding mode control grey wolf optimization algorithm offshore photovoltaic cells

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Federated learning and GWO-enabled consumer-centric healthcare internet of things for pancreatic tumour

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ALEXANDRIA ENGINEERING JOURNAL 2025年 122卷 344-354页

作者： Alruwais, Nuha Elhessewi, Ghada Moh. Samir Saeed, Muhammad Kashif Alshammeri, Menwa Alrusaini, Othman Alkharashi, Abdulwhab Al Zanin, Samah Said, Yahia King Saud Univ Coll Appl Studies & Community Serv Dept Comp Sci & Engn POB 22459 Riyadh 11495 Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Hlth & Rehabil Sci Dept Hlth Sci POB 84428 Riyadh 11671 Saudi Arabia King Khalid Univ Appl Coll Dept Comp Sci Muhayil Saudi Arabia Jouf Univ Coll Comp & Informat Sci Dept Comp Sci Sakakah Saudi Arabia Umm Al Qura Univ Makkah Appl Coll Dept Engn & Appl Sci Mecca Saudi Arabia Saudi Elect Univ Dept Comp Sci Coll Comp & Informat Riyadh Saudi Arabia Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci Al Kharj Saudi Arabia Northern Border Univ Ctr Sci Res & Entrepreneurship Ar Ar 73213 Saudi Arabia

In order to get a correct diagnosis and choose the best treatment options before it becomes deadly, early detection and classification of pancreatic tumours are essential. Grading can be a tedious and time-consuming process for experts and doctors when the case is complex. In such cases, experts usually look at the tumour and pinpoint its exact position. Moreover, it could be required to compare the tumor's cells to those in the vicinity. The end goal is to confirm that the growth is a tumour and, if possible, to ascertain the exact type and grade of the tumour. However, due to the high amounts of weights sent and received from the client-side trained models, federated learning techniques incur substantial communication overhead. This study aims to address this problem by introducing a unified framework that integrates the inherent capabilities of Federated Learning (FL) with the unique characteristics of the grey wolf Optimisation algorithm. The pancreatic tumour dataset is used to evaluate the GWO-enabled FL framework. The proposed model was more network efficient, performed better in data imbalance scenarios, and led to lower communication costs than the currently available federated average model. Following validation, the proposed framework attained a prediction accuracy of 98.9%. For pancreatic tumour classification, the data obtained from the proposed system can be a useful component.

关键词： Pancreatic Tumour Classification Federated Learning grey wolf optimization algorithm CNN

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Capacity configuration and control optimization of off-grid wind solar hydrogen storage system

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ENERGY 2025年 324卷

作者： Zhao, Yuyang Zhao, Yuhuan Cao, Song Zhu, Zhengzheng Sun, Hexu Hebei Univ Sci & Technol Sch Elect Engn Shijiazhuang 050018 Peoples R China Hebei Engn Lab Wind Power & Photovolta Coupling Hy Shijiazhuang 050018 Peoples R China

The configuration and operational validation of wind solar hydrogen storage integrated systems are critical for achieving efficient energy utilization, ensuring economic viability, and maintaining system stability. This study proposed an off-grid multi-energy system capacity configuration and control optimization framework based on the grey wolf optimization (GWO) algorithm, which enhances system revenue through an improved capacity allocation model. The results demonstrate the following: Firstly, the proposed system achieves a significant financial improvement, with an annual revenue increase of 33.79 % compared to a hydrogen production scheme relying solely on wind power without energy storage. Secondly, the adoption of a wind solar complementary hydrogen production approach increases the annual revenue of the system by 33.33 % compared to the single wind power hydrogen production scheme. Furthermore, a system operation control strategy based on the State of Charge (SOC) of lithium batteries is proposed. Using operational data from the Zhangjiakou Chongli wind solar complementary coupling hydrogen production project, the effectiveness of the proposed control strategy is validated, demonstrating its ability to ensure stable system operation. Results show that in wind and solar power excess scenarios, the SOC rises from 45.8 % to 49.8 %, and declines to 47.2 % when there's a power deficit, indicating its potential for peak shaving and valley filling, thereby mitigating fluctuations in wind and solar power output.

关键词： Wind power Solar power Energy storage Capacity configuration Power prediction grey wolf optimization algorithm

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A novel structural adaptive seasonal grey Bernoulli model in natural gas production forecasting

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 148卷

作者： Yan, Shuli Peng, Mengna Wu, Lifeng Xiong, Pingping Nanjing Univ Informat Sci & Technol Res Inst Risk Governance & Emergency Decis Making Collaborat Innovat Ctr Forecast & Evaluat Meteorol Sch Management Sci & Engn Nanjing 210044 Peoples R China Univ Granada Dept Comp Sci & Artificial Intelligence Granada 18071 Spain Hebei Univ Engn Coll Management Engn & Business Handan 056038 Peoples R China

Energy data are characterized by nonlinearity, seasonality and growth, which render prediction challenging, making it difficult for Chinese government to formulate relevant policies. Considering that the grey Bernoulli model can deal with nonlinear and growing data, and utilizing the newly proposed seasonal factor, we propose a novel structural adaptive seasonal grey Bernoulli model, which is limited to application to nonlinear, seasonal, and growing time sequence. Firstly, the traditional seasonal factors are logarithmically transformed to ensure more refined seasonal factor sequence. Then, the grey Bernoulli model and the grey structural adaptive model are combined in a multiplicative manner to create a new model, thereby refining its structural adaptability. And the grey wolf optimization algorithm is used to optimize the parameters in the model. Finally, the natural gas production prediction is implemented to verify the feasibility of this model. The mean absolute percentage errors of the proposed model are below 5% both in the experiment and in the robustness test. Compared with other models, this model demonstrates an improvement of 29.75% in prediction accuracyand 15.80% in robustness test. Additionally, the Diebold & Mariano statistics exhibit values that are essentially significant at the 10% level. The experimental results substantiate the validity, superiority, and robustness of the model proposed in this paper, which can be effectively applied to the prediction of seasonal natural gas production.

关键词： grey Bernoulli model Structural adaptive grey model Seasonal natural gas production grey wolf optimization algorithm

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