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Multi-strategy enhanced grey wolf algorithm for obstacle-aware WSNs coverage optimization

AD HOC NETWORKS 2024年 152卷

作者： Wang, Zhendong Huang, Lili Yang, Shuxin Luo, Xiao He, Daojing Chan, Sammy Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou 341000 Jiangxi Peoples R China Jiangxi Univ Sci & Technol Sch Elect Engn & Automat Ganzhou 341000 Jiangxi Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong 999077 Peoples R China

Moving sensor nodes can mitigate the coverage problem of random deployment in wireless sensor networks. However, the movement of nodes affects the lifetime and integrity of the network. Therefore, both energy saving and efficient coverage are crucial factors. In this paper, we propose an energy-efficient coverage optimization technique with the help of the multi-Strategy grey wolf optimization (MSGWO) algorithm. This method can reduce energy consumption and improve coverage area by mixing higher-order multinomial sensing models and a sort-driven hybrid opposition-based learning. In addition, node movement and boundary strategies are proposed to help nodes jump out of obstacles when facing obstacle-aware deployments. The MSGWO is validated and compared on several classical test functions, and the results show that the MSGWO performs well. The MSGWO algorithm is applied to optimize the WSN coverage on different obstacle scenarios, the experimental results show that the algorithm helps to increase the network coverage from 84 % to 97.86 %, extends the network lifecycle by 50 %, reduces the cost of node deployment, and the network has good connectivity and scalability.

关键词： Hybrid higher-order coverage awareness model grey wolf optimization algorithm Obstacles Coverage optimization Network lifetime

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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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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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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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Research on dynamic flatness feedback control strategy based on IGWO control efficiency identification for cold tandem rolling mill

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2025年 1-21页

作者： Zhou, Xiaomin Li, Liqi Wang, Shuaikun Xiong, Qingxia Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Shunde Innovat Sch Foshan 528399 Peoples R China

Flatness control is an important means to improve the quality of the strip, and modern tandem cold rolling mills are equipped with various control actuators. The existing flatness control efficiency is determined offline and remains constant throughout the manufacturing process. During actual manufacturing, equipment, and process states may change, making it necessary to identify flatness control efficiency online. The main flatness feedback control strategy is a fixed priority sequence in the existing CVC (Continuous Variable Crown) cold tandem rolling mill. The control system does not have the flexibility to adjust the sequence based on real-time flatness. Aiming at the above problems, in order to improve the effect of flatness control, this paper proposes a dynamic flatness feedback control strategy based on control efficiency identification. To address the issue of high cost and difficulty in updating the on-site flatness control efficiency, based on historical actual data, the IGWO (improved grey wolf optimization) algorithm is used to intelligently identify control efficiency. Data post-processing through IF (isolated forests) and CLT (central limit theorem) obtains results on control efficiency. At the same time, a similarity index is proposed to dynamically adjust the priority sequence of feedback control based on the identification results of the control efficiency and the measured flatness deviations. Finally, the field application results show that the flatness control accuracy is improved, the reduction value of the flatness deviations increased by 13.41%, and the reduction rate of the flatness deviations increased by 9.66%.

关键词： Flatness control efficiency grey wolf optimization algorithm Flatness feedback control Feedback control strategy

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A novel time-varying parameters structural adaptive Hausdorff fractional discrete grey model and its application in renewable energy production and consumption prediction

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ENERGY 2025年 318卷

作者： Wang, Yong Zhang, Zejia Wang, Yunhui Sun, Lang Yang, Rui He, Wenao Sapnken, Flavian Emmanuel Li, Hong-Li Southwest Petr Univ Sch Sci Chengdu 610500 Sichuan Peoples R China Neijiang Normal Univ Key Lab Numer Simulat Sichuan Prov Univ Sch Math & Informat Sci Neijiang 641000 Sichuan Peoples R China Univ Douala Univ Inst Technol Lab Technol & Appl Sci POB 8698 Douala Cameroon Xinjiang Univ Coll Math & Syst Sci Urumqi 830017 Xinjiang Peoples R China

The role of energy is essential in driving human development and achieving economic and social progress. It's crucial to make precise predictions about how traditional and new energy sources will be used in the future, as this information can help a country create effective energy strategies and make adjustments to its energy structure accordingly. Considering the common presence of volatility, nonlinearity and periodicity in long-term energy data series, this paper proposes a novel time-varying parameters adaptive discrete grey prediction model FHATDGM (1,1) based on Hausdorff fractional derivative. This model further explores the possible periodic features in the system, in order to more comprehensively and accurately describe the development trend of the time series. In addition, the grey wolf optimization algorithm (GWO) is selected to optimize the parameters of the model. Then, for the robustness of the results obtained by introducing intelligent optimization algorithm, this paper uses the method of combining Monte Carlo simulation and probability density analysis to test, indicating that the proposed model has better stability and accuracy compared with the existing six models. Finally, three actual cases of China's solar power generation, United States hydroelectric power generation and China's consumption of coal are predicted. The results show that the FHATDGM (1,1) has the highest fitting and forecasting accuracy. The reduction in prediction error compared to the other six models ranged from 1.46 % to 37.49 %.

关键词： grey model grey wolf optimization algorithm Hausdorff fractional derivative Monte-carlo simulation Renewable energy production and consumption

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Construction of source load uncertainty economic dispatch model based on distributed robust opportunity constraints

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Energy Informatics 2025年第1期8卷 1-14页

作者： Li, Jinjian Information Center Guangxi Zhuang Autonomous Region Company of China National Tobacco Corporation Nanning530000 China

With the increasing demand for electricity, the power system is facing enormous challenges. To ensure the equilibrium between supply and demand in the electricity market and the safety and stability of the power grid, a source load uncertainty economic dispatch model based on distributed robust opportunity constraints is proposed to cope with the uncertainty of sustainable energy resources such as wind power and photovoltaics. By introducing an improved Elman network and grey wolf optimization algorithm, high-precision prediction of short-term loads is achieved, providing data support for scheduling models. The experiment outcomes indicate that the prediction model grounded on the improved Elman network and grey wolf optimization algorithm performs the best in scheduling performance on both the training and testing sets, with the lowest cost, the highest utilization rates of wind and solar power, and the lowest probability of constraint default. In addition, the economic dispatch model proposed by the research has significant advantages in reducing total dispatch costs, improving wind and photovoltaic utilization rates, and constraining default probability control. In typical load scenarios, the total scheduling cost of the model is $1,308,469, with wind and photovoltaic utilization rates reaching 90.5% and 86.1% respectively, and a default probability of only 0.9%. The research results indicate that the model exhibits superiority in real-time response time, especially suitable for scenarios with high load fluctuations. The research provides important theoretical basis and application value for the economic dispatch of power systems. © The Author(s) 2025.

关键词： Distributed robust opportunity constraint Elman network grey wolf optimization algorithm Power system Source load uncertainty

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An improved grey wolf algorithm and its localization research in complex indoor environments

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SCIENTIFIC REPORTS 2025年第1期15卷 1-23页

作者： Li, Bing Hao, Yanxin Cui, Yiyang Chai, Xingshao Zhou, Jingmei Liu, Chungang Hebei Normal Univ Coll Combust Engn Shijiazhuang 050024 Peoples R China Hebei Prov Key Lab Informat Fus & Intelligent Cont Shijiazhuang 050024 Peoples R China Hebei Prov Innovat Ctr Wireless Sensor Network Dat Shijiazhuang 050024 Peoples R China

In complex indoor environments, traditional localization methods often suffer from non-line-of-sight (NLOS) and multipath problems, which lead to unsolvable or incorrectly solved mathematical localization models, thereby limiting localization accuracy. A localization method based on swarm intelligence optimization has been proposed to address this issue. The swarm intelligence optimization algorithm does not require solving matrix inversions and transforms the localization problem into a function optimization problem, which can obtain approximate optimal solutions. Nevertheless, optimization algorithms are beset with issues like sluggish convergence speed and proneness to getting trapped in local optima, thereby failing to satisfy the current practical requirements for localization. This paper proposes a new method that applies the grey wolf optimization (GWO) algorithm to ultra-wideband (UWB) indoor localization to enhance localization accuracy. It improves the GWO algorithm with four strategies. Firstly, a small-area optimization strategy near the target point is proposed. The Chan algorithm is adopted for initial tag localization, and the initial localization result is taken as a constraint to construct the search area of the GWO algorithm, thereby reducing the large space region to a small space region and enhancing optimization efficiency. Secondly, an improved Tent mapping, a nonlinear convergence factor, a fitness-weighted location update strategy, and an out-of-bounds reflection mechanism are designed to improve the GWO algorithm, referred to as the TIGWO algorithm. Finally, apply the TIGWO algorithm to determine the optimal location of the tag. The experimental results indicate that the proposed algorithm significantly enhances indoor localization accuracy. Compared to the Chan, Chan-Taylor, PSO, WOA, and GWO algorithms, the average localization accuracy has been enhanced by 59.65%, 63.41%, 40.97%, 45.97%, and 35.44%, respectively. In an equipment warehouse

关键词： Ultra-wideband technology 3D indoor localization Swarm algorithm grey wolf optimization algorithm Localization accuracy

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Performance optimization of high-rise residential buildings in cold regions considering energy consumption

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DISCOVER APPLIED SCIENCES 2025年第2期7卷 1-14页

作者： Song, Liwei Jilin Technol Coll Elect Informat Acad Affairs Off Jilin 132021 Peoples R China

With the acceleration of urbanization, high-rise residential buildings has become a significant aspect of urban living. However, while high-rise residential buildings provide much housing, they also bring significant energy consumption. To raise the energy utilization efficiency of high-rise residential buildings, reduce energy consumption, and achieve sustainable development, this study focuses on high-rise residential buildings in cold regions. Through methods such as parametric modeling, joint simulation of building performance, multi-objective optimization algorithms, and improved grey wolf optimization algorithms, multi-objective optimization experiments are conducted to achieve optimal energy-saving effects. The outcomes denote that the average energy consumption of buildings remains at around 20.5 kW h/m2, and the maximum value of the last generation thermal comfort solution set is maintained at 62%, while the minimum value is maintained at 58%. The improved grey wolf optimization algorithm reduces training time, has better predictive ability, and can more accurately characterize changes in energy consumption of high-rise buildings. This study provides practical design methods and strategy references for high-rise residential buildings in the design phase by analyzing data, mining patterns, and summarizing design strategies.

关键词： Energy consumption High-rise residential buildings grey wolf optimization algorithm Multi-objective optimization Enclosure structure

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optimization of Flight Endurance for Turboprop Air Taxis Using Metaheuristic algorithms

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INTERNATIONAL JOURNAL OF ENGINEERING 2025年第7期38卷 1685-1698页

作者： Talouki, I. Fozouni Toloei, A. Shahid Beheshti Univ Dept New Technol & Aerosp Engn Tehran Iran

This study aims to optimize the flight endurance of a 12-passenger turboprop air taxi using two metaheuristic optimization algorithms: grey wolf optimization (GWO) and Ant Colony optimization (ACO). Initially, the gradient descent method was employed to estimate the aircraft's maximum weight. Subsequently, the aircraft's performance characteristics were utilized as design variables and flight endurance was optimized under specific constraints without altering the physical structure of the aircraft. The optimization process was implemented, and the results were evaluated and compared in terms of performance and efficiency. This research demonstrated that the two mentioned algorithms, utilizing random and collective strategies, were able to enhance the aircraft's efficiency. Additionally, the optimization of flight endurance for three real aircraft Piper, Beechcraft, and Bombardier was examined compared to their original endurance. In this context, the Ant Colony optimization algorithm exhibited better performance than the grey wolf optimization algorithm, which could have a positive impact on flight operations without refueling or the process of finding alternative airports.

关键词： Air Taxi optimization Gradient Descent grey wolf optimization algorithm Ant Colony optimization algorithm

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