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Prediction of landfill gases concentration based on Grey wolf optimization - Support Vector Regression during landfill excavation process

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WASTE MANAGEMENT 2025年 198卷 128-136页

作者： Liu, Zhansheng Zhang, Zehua Zhang, Qingwen Zhao, Linlin Beijing Univ Technol Coll Architecture & Civil Engn Beijing 100124 Peoples R China Beijing Univ Technol Key Lab Urban Secur & Disaster Engn Minist Educ Beijing 100124 Peoples R China

In some areas, there is a phenomenon that the landfill is full or even over-capacity with the extension of the service period. With the aging and damage of the protective facilities, this phenomenon may have a more serious impact on the surrounding environment. It is necessary to excavate and transport the waste beyond the part to control it. This process will inevitably produce many landfill gas emissions, which will pollute the air. Therefore, it is necessary to predict and control the landfill gas. This study utilizes the Grey wolf optimization (GWO) algorithm to optimize Support Vector Regression (SVR). It establishes prediction models for various LFG concentrations based on previous LFG concentration data and real-time environmental monitoring data. The models are compared with traditional Support Vector Regression and Random Forest (RF) algorithms, predicting the concentrations of odor, ammonia, hydrogen sulfide, methane, and nitrogen oxides. The results indicate that GWO-SVR demonstrates more stable and accurate predictions across various LFG, with the coefficient of determination R2 approximately 10% higher than that of SVR and RF, and most other error metrics significantly lower. In contrast, SVR and RF show substantial errors in predicting odor, hydrogen sulfide, and nitrogen oxides. Thus, the GWO-SVR algorithm substantially improves the performance in predicting LFG concentrations, meeting the needs of on-site management.

关键词： Landfill governance Landfill gas prediction gray wolf optimization algorithm Support vector regression

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GWO-elman based composite detection of transformer oil flow temperature

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OPTICAL FIBER TECHNOLOGY 2025年 89卷

作者： Du, Jiwei Zhao, Yi Lan, Binhuan Huang, Liming Sun, Shizheng Suining Power Supply Co State Grid Sichuan Elect P Suining 629000 Sichuan Peoples R China Chongqing Jiaotong Univ Sch Elect & Mech Engn & Vehicle Engn Chongqing 400074 Peoples R China

This paper addresses the issue of nonlinear coupling error caused by cross-sensitivity of strain and temperature in the composite detection of flow rate and temperature in transformer oil using a Fiber Bragg Grating (FBG) sensor. This paper focuses on the FBG sensor as the research object, elucidating the underlying principles of composite detection. Subsequently, a composite detection experimental platform is established for the purpose of analyzing the coupling error of flow rate and temperature. Ultimately, a nonlinear decoupling algorithm based on the Grey wolf Optimizer (GWO) is proposed to enhance the nonlinear decoupling algorithm of the Elman neural network (simple recurrent neural network, Elman). The findings demonstrate that within the flow rate range of 0-5 m/s and the temperature range of 30 degrees C-150 degrees C, the maximum error is reduced by 72.0 % and 81.3 %, and the average error is reduced by 74.4 % and 79.4 %. This significantly enhances the precision and reliability of the sensor's detection capabilities.

关键词： Fiber Bragg grating Flow rate temperature complex detection Strain-temperature cross-sensitivity gray wolf optimization algorithm Elman neural network

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Short-term photovoltaic power prediction based on RF-SGMD-GWO-BiLSTM hybrid models

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ENERGY 2025年 316卷

作者： Yang, Shaomei Luo, Yuman North China Elect Power Univ Dept Econ & Management 689 Huadian Rd Baoding 071000 Peoples R China

Accurate photovoltaic (PV) power prediction can support optimal scheduling and decision-making in energy systems. An innovative hybrid prediction model efficiently predicts complex time series data by integrating multiple advanced algorithms in a deep fusion approach. First, Random Forest (RF) is employed for feature screening and optimization to eliminate redundant features, thereby enhancing model training efficiency and prediction accuracy when dealing with complex environmental data. Subsequently, the symplectic geometry model decomposition (SGMD) technique was utilized to break down historical power signals, extracting information from various frequency components and enhancing the input features for the Bidirectional Long and Short-Term Memory Network (BiLSTM) model. Thereafter, aiming at the problem of hyperparameter tuning of the BiLSTM model, the gray wolf optimization algorithm (GWO) was used for automatic optimization to improve prediction stability. In the case studies, the proposed model exhibited impressive performance metrics: in Case Study 1, it achieved an RMSE of 1.351, an MAE of 0.666, and a MAPE of 3.786, while in Case Study 2, it recorded an RMSE of 0.487, an MAE of 0.265, and a MAPE of 6.304. The model also shows scalability and robustness across diverse climatic conditions and PV technologies, confirming its applicability to real-world scenarios.

关键词： Power prediction Feature selection gray wolf optimization algorithm Bidirectional long and short-term memory networks

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Research on Mobile Robot Path Planning Based on MSIAR-GWO algorithm

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SENSORS 2025年第3期25卷 892-892页

作者： Chen, Danfeng Liu, Junlang Li, Tengyun He, Jun Chen, Yong Zhu, Wenbo Foshan Univ Coll Mech Engn & Automat Foshan 528000 Peoples R China

Path planning is of great research significance as it is key to affecting the efficiency and safety of mobile robot autonomous navigation task execution. The traditional gray wolf optimization algorithm is widely used in the field of path planning due to its simple structure, few parameters, and easy implementation, but the algorithm still suffers from the disadvantages of slow convergence, ease of falling into the local optimum, and difficulty in effectively balancing exploration and exploitation in practical applications. For this reason, this paper proposes a multi-strategy improved gray wolf optimization algorithm (MSIAR-GWO) based on reinforcement learning. First, a nonlinear convergence factor is introduced, and intelligent parameter configuration is performed based on reinforcement learning to solve the problem of high randomness and over-reliance on empirical values in the parameter selection process to more effectively coordinate the balance between local and global search capabilities. Secondly, an adaptive position-update strategy based on detour foraging and dynamic weights is introduced to adjust the weights according to changes in the adaptability of the leadership roles, increasing the guiding role of the dominant individual and accelerating the overall convergence speed of the algorithm. Furthermore, an artificial rabbit optimization algorithm bypass foraging strategy, by adding Brownian motion and Levy flight perturbation, improves the convergence accuracy and global optimization-seeking ability of the algorithm when dealing with complex problems. Finally, the elimination and relocation strategy based on stochastic center-of-gravity dynamic reverse learning is introduced for the inferior individuals in the population, which effectively maintains the diversity of the population and improves the convergence speed of the algorithm while avoiding falling into the local optimal solution effectively. In order to verify the effectiveness of the MSIAR-GWO a

关键词： path planning gray wolf optimization algorithm reinforcement learning parameter selection detour foraging

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A novel combination prediction model of ultra-short-term wind speed

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WIND ENGINEERING 2025年第1期49卷 249-267页

作者： Tang, Fei Shenyang Univ Technol Coll Artificial Intelligence Room 503b111Shenliao West RdEcon & Technol Dev Shenyang 110870 Peoples R China

The accurate prediction of ultra-short-term wind speed has important theoretical significance and practical application value. In this paper, a combination prediction model of ultra-short-term wind speed based on variational mode decomposition is proposed. Firstly, the variational mode decomposition algorithm is introduced to decompose the ultra-short-term wind speed and obtain the components of different frequency. The approximate entropy algorithm is used to calculate the complexity of each component. According to the calculation results, echo state network is selected to predict high complexity components, support vector machine is used to predict medium complexity components, and autoregressive integrated moving average model is used to predict low complexity components. Then, the predicted values of each component are added to get the final prediction result. gray wolf algorithm is used to optimize the model parameters of support vector machine and echo state network. In addition, the approximate entropy calculation results show that compared with the original ultra-short-term wind speed, the complexity of the components obtained by the variational mode decomposition algorithm is reduced, which makes the modeling of the ultra-short-term wind speed system simpler. Finally, the validity of the model is verified by taking the ultra-short-term wind speed data of 5 minutes and 10 minutes sampling period actually collected as the research object. The results show that the prediction model proposed in this paper has better performance than other single or combination prediction models.

关键词： Ultra-short-term wind speed variational mode decomposition combination prediction approximate entropy gray wolf optimization algorithm

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Toward an autonomic approach for Internet of Things service placement using gray wolf optimization in the fog computing environment

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SOFTWARE-PRACTICE & EXPERIENCE 2021年第8期51卷 1745-1772页

作者： Salimian, Mahboubeh Ghobaei-Arani, Mostafa Shahidinejad, Ali Islamic Azad Univ Qom Branch Dept Comp Engn Qom Iran

Divers and the huge amount of data produced by the Internet of Things (IoT) applications on the one hand, and inherent limitations of local equipment to handle these data, on the other hand, leads to present emerging closer technologies to the end-users such as fog computing environment. Nevertheless, despite the numerous advantages of such an environment, it still needs state-of-the-art approaches to cope with some inherent limitations. In the literature, resource placement strategies are generally proposed to address such problems, in which the IoT applications are mapped to fog nodes. However, despite its importance, different approaches attempt to enhance the overall system's performance and users' expectations: none of such approaches is satisfactory. In this article, to deploy IoT applications on fog nodes, an autonomic IoT service placement approach based on the gray wolf optimization scheme is proposed, enhancing the system's performance while considering execution costs. Besides, the autonomic concepts help make an appropriate automanagement system that fits better the fog environment's dynamic behavior. Simulation results demonstrate that the proposed approach outperforms the other approaches and converges to the solution in near-optimal application deployment on fog nodes in respect of the performance of performing services that are 93.7%, the performance of the average waiting time for performed services that are 100%, the remaining services sent to an extra provisioned period that is zero.

关键词： autonomic computing fog computing gray wolf optimization algorithm IoT applications service placement

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An efficient and reliable service customized routing mechanism based on deep learning in IPv6 network

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IET COMMUNICATIONS 2022年第19期16卷 2249-2264页

作者： Wang, Yufu Wang, Xingwei Yi, Bo Huang, Min Northeastern Univ Coll Comp Sci & Engn 3-11 Wenhua Rd Shenyang Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang Peoples R China

Best-effort service model of traditional routing is gradually hard to meet the personalized demands under the rapid development of network technologies (e.g. 5G and IPv6). Therefore, service customization should be considered. In this work, a service customized routing mechanism based on deep learning in IPv6 network is proposed, which includes deep learning-based service customization module, reliability evaluation module, and routing calculation module. The first module uses neural network to learn the complex service customization function, which can quickly output win-win customized service strategies based on user demands. The second module can quantify the reliability of service routing paths, where not only the link status of IPv6 Neighbor Unreachable Detection (NUD) is considered, but also propose link performance weights to ensure the reliability of differentiated service performance. The third module uses the gray wolf optimization algorithm to calculate an optimal routing path to forward services with the customized strategies as the constraints and the maximum reliability and minimum cost as the goal. Finally, the mechanism is tested on the IPv6 Source Address Validation Improvement (SAVI) platform, which can reduce the execution time by 12.25% and improve the average routing reliability, user and ISP satisfaction by 9.0%, 40.45% and 7.4%, respectively.

关键词： personalized demands optimal routing path computer network reliability differentiated service performance IP networks complex service customization function telecommunication network routing customer services maximum reliability deep learning (artificial intelligence) optimisation network technologies service routing paths best-effort service model reliability evaluation module average routing reliability IPv6 Source Address Validation Improvement platform IPv6 neighbor unreachable detection computer network security forward services routing calculation module neural network deep learning-based service customization module reliable service customized routing mechanism gray wolf optimization algorithm customized strategies IPv6 network efficient service customized routing mechanism customized service strategies

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Research on Improving gray wolf algorithm Based on Multi-Strategy Fusion

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IEEE ACCESS 2023年 11卷 66135-66149页

作者： Yang, Xiaoxiao Qiu, Yihui Xiamen Univ Technol Management Sci Xiamen Peoples R China Xiamen Univ Technol Xiamen Peoples R China Xiamen Univ Technol Sch Econ & Management Xiamen Peoples R China

To address the shortcomings of the basic gray wolf optimization (GWO) algorithm in solving complex problems, such as relying on the initial population, converging too early, and easily falling into local optimality, a chaotic reverse learning initialization strategy, a nonlinear control parameter convergence strategy, and a dynamic position update strategy are introduced to develop a multi-strategy fusion Improved gray wolf optimization (IGWO) algorithm, and this method is used to solve function optimization problems. First, a chaotic backward learning initialization strategy, based on logistic mapping and backward learning, is adopted to improve the random initialization of the GWO algorithm and enhance the traversal and diversity of the initial population. Second, a nonlinear control parameter for local perturbation is constructed to avoid the problem of premature convergence of the GWO algorithm due to linear convergence and to balance the exploration and exploitation ability of the GWO algorithm. Finally, a location guidance strategy based on dynamic weights and individual memory is proposed to effectively improve the algorithm's optimization accuracy and computational efficiency;meanwhile, the Gaussian-Cauchy mutation strategy of superior selection is introduced to optimize the location update of optimal individual a wolves and improve the ability of the population to jump out of local extremes. Simulation experiments are conducted for 11 classical test functions, and the results show that the proposed improved algorithm IGWO for gray wolves is superior to 10 other standard swarm intelligence optimization algorithms and 4 other improved optimization algorithms in terms of solution accuracy, convergence speed, and algorithm stability. It provides a new optimization algorithm for solving complex optimization problems.

关键词： gray wolf optimization algorithm population intelligence optimization chaotic mapping lenticular imaging backward learning nonlinear control parameters

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Digital watermarking algorithm for QR code based on hybrid PSOGWO optimization

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PHYSICA SCRIPTA 2024年第11期99卷

作者： Han, Yue Lv, Yuanjian Wang, Zhifang Heilongjiang Univ Dept Elect Engn Xuefu Rd Harbin 150080 Peoples R China

With the rapid development of digital media, QR codes have become widely used as an important medium for information transmission. However, this also presents serious security challenges, such as frequent image infringement and inevitable compression and scaling issues during data transfer. These challenges directly threaten the effectiveness and integrity of image information, and effective protection measures must be taken. Under this background, digital watermarking technology comes into being as an innovative method to enhance security. This paper presents an innovative Hybrid Particle Swarm optimization and gray wolf optimization (HPG) algorithm, which combines the advantages of PSO and overcomes the limitations of traditional GWO algorithm. The algorithm is further improved by using Bernoulli chaotic mapping and nonlinear convergence factor. The experiments focus on the global and local search balance of the algorithm and its ability to resist repeated scaling attacks. The results show that the nonlinear convergence factor of HPG algorithm slows down the iteration speed at the initial stage, strengthens the global search, and avoids falling into the local optimal. In the later stage, the iteration is accelerated to improve the local search speed and convergence efficiency, thus optimizing the performance of the algorithm and ensuring the safety and quality of image information. In addition, the algorithm has better performance in balancing imperceptibility and robustness of digital watermarking, and its peak signal-to-noise ratio (PSNR) is higher. Even with many scaling iterations, the normalized correlation (NC) value is still close to 1. Compared with the existing typical algorithms, HPG algorithm is more robust to repeated scaling attacks.

关键词： watermark embedding algorithm gray wolf optimization algorithm nonlinear control parameters particle swarm algorithm

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A comparative analysis of hybrid RF models for efficient lithology prediction in hard rock tunneling using TBM working parameters

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ACTA GEOPHYSICA 2024年第3期72卷 1847-1866页

作者： Zhou, Jian Yang, Peixi Yong, Weixun Khandelwal, Manoj Huang, Shuai Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Federat Univ Australia Inst Innovat Sci & Sustainabil Ballarat Vic 3350 Australia

With the escalating demand for underground mining and infrastructure construction, the optimization of tunnel construction has emerged as a primary concern for researchers. The geological conditions encountered during the excavation of hard rock tunnels using tunnel boring machines (TBM) significantly impact construction efficiency and cost-effectiveness. The existing lithology testing methods need to be more efficient in aligning with TBM operational efficiency. In recent years, the rapid advancement of artificial intelligence has paved the way for its integration into numerous domains, including tunnel engineering. To address this issue, this study proposes three innovative hybrid RF-based intelligent models, namely PSO-RF, ALO-RF, and GWO-RF, for the precise prediction of lithology in hard rock tunnels using TBM working parameters. The TBM operating parameters of the Jilin Yinsong Water Supply Project serve as the basis for this investigation. Twelve distinct characteristic parameters relevant to the lithology of the tunnel working face were carefully selected as input parameters for lithology prediction. Comparative analysis of the three hybrid models reveals that GWO-RF demonstrates exceptional lithology prediction performance (ACC = 0.999924;PREA = 0.0.9999976;RECA = 0.999775;F1A = 0.999876;Kappa = 0.999911), whereas PSO-RF and ALO-RF exhibit slightly inferior performance. Nonetheless, all three hybrid models exhibit a significant improvement in prediction accuracy compared to the unoptimized RF model. The research findings presented herein facilitate the swift determination of TBM working surface lithology, enabling timely adjustment of TBM working parameters, reducing equipment wear and tear, and enhancing construction efficiency.

关键词： Tunnel boring machine Lithology prediction Random forest Particle swarm optimization algorithm Ant-lion optimization algorithm gray wolf optimization algorithm

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