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Research progress of machine-learning algorithm for formation pore pressure prediction

PETROLEUM SCIENCE AND TECHNOLOGY 2025年第4期43卷 341-359页

作者： Pan, Haoyu Deng, Song Li, Chaowei Sun, Yanshuai Zhao, Yanhong Shi, Lin Hu, Chao Changzhou Univ Coll Petr Engn Changzhou 213000 Peoples R China Kunlun Digital Technol Co Ltd Beijing Peoples R China

Formation pore pressure is one of the most important basic data in petroleum exploration and development. The traditional prediction model of formation pore pressure relies on artificial experience and is highly subjective, so it seldom considers the influence multiple variables characteristics of formation pore pressure. In recent years, machine learning has been applied in lithology, reservoir, complex drilling conditions, formation pore pressure, and other fields. This article introduces the research progress and general process of machine learning algorithm in formation pore pressure prediction in recent years. In this study, the intelligent optimization algorithm is used to optimize the machine learning model and realize the intelligent prediction method of formation pore pressure. The results show that support vector regression (SVR) obtained the best prediction performance with the determination coefficient of 0.996. At the same time, this study reflects the importance of intelligent optimization algorithm to machine learning model optimization accuracy. This method provides a new idea for using multiple variables formation pore pressure in the future.

关键词： formation pore pressure intelligent optimization algorithm machine learning multiple variables petroleum exploration

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MSGJO: a new multi-strategy AI algorithm for the mobile robot path planning

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2025年第9-10期137卷 5081-5109页

作者： Wang, Baiyi Zhang, Zipeng Andriukaitis, Darius Liu, Xinhua Hua, Dezheng Li, Zhixiong Vashishtha, Govind Chauhan, Sumika China Univ Min & Technol Sch Mech & Elect Engn Xuzhou Peoples R China Kaunas Univ Technol Fac Elect & Elect Engn Dept Elect Engn LT-44249 Kaunas Lithuania Opole Univ Technol Dept Mfg Engn & Automat Prod PL-45758 Opole Poland Wroclaw Univ Sci & Technol Fac Geoengn Min & Geol Na Grobli 15 PL-50421 Wroclaw Poland Graph Era Deemed Be Univ Dept Mech Engn Dehra Dun 248002 India Lovely Profess Univ Div Res & Dev Phagwara 144411 India

This paper aims to implement the path planning problem for mobile robot in complex environments by golden jackal optimization algorithm (GJO). To address the shortcomings of original GJO algorithm, such as poor exploitation ability and ease of getting stuck in a local optimal region, a multi-strategy improved GJO algorithm (MSGJO) is proposed. Firstly, the random selection strategy of the nonlinear composite adaptive convergence factor is designed to realize the reasonable balance between the exploration ability and the exploitation ability of GJO algorithm. Furthermore, an enhanced sparrow explorer location update strategy is proposed to further search the optimal individual of current iteration, which improves the efficiency of GJO algorithm moving in target direction. Then, the genetic algorithm's variation strategy is added to randomly transform the disadvantaged individuals in the population into the dominant individuals. The performance of MSGJO algorithm is verified both on classical and CEC test functions (CEC2019 and CEC2022). Meanwhile, when applied to path planning problems, the proposed algorithm can effectively obtain the global optimal and smoother path. Through the results of simulations and experiments, it is demonstrated that the proposed algorithm has better performance than other compared algorithms in environment with both static and dynamic obstacles, which proving the global convergence, robustness, and local obstacle avoidance ability of the proposed algorithm.

关键词： intelligent manufacturing Industrial robots Robot path planning intelligent optimization algorithm

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Inverting Single Crystal Elastic Constants of Polycrystals with Metallographic Measurement and Modified fi ed Ultrasonic Backscatter Mode

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MATERIALS TRANSACTIONS 2024年第9期65卷 1116-1125页

作者： Zhang, Xia Li, Xiongbing Xiangnan Univ Coll Comp & Artificial Intelligence Chenzhou 423000 Hunan Peoples R China Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Hunan Peoples R China

Single crystal elastic constants are vital parameters associated with the mechanical properties of polycrystals. To accurately invert these constants, precise experimental techniques must be employed. However, traditional ultrasonic measurement methods can introduce errors when dealing with complex microstructures. To address this issue, we propose an approach that combines metallographic measurements, homogenization techniques, and intelligent algorithms to invert the single crystal elastic constants of polycrystals. First, we developed a modified fi ed single scattering response (SSR) model based on metallographic measurements and the best-fitted fi tted spatial correlation function (SCF). In addition, different ff erent homogenization techniques were compared to enhance the accuracy of the model. We then propose an Adaptive Probability Differential ff erential Evolution Quantum Particle Swarm optimization (AP-DE-QPSO) algorithm to enhance the accuracy of optimization results with an adaptive probability (AP) operator in the optimization process. After verification fi cation with three standard test functions, we demonstrated that the algorithm achieved higher accuracy. Finally, by utilizing the AP-DE-QPSO algorithm and fi tting the spatial variance of the modified fi ed SSR model to the ultrasonic backscattering experimental data, we successfully inverted the global optimal solution of the single crystal elastic constants. The Self- Consistent averaging technique yielded the best results, with relative errors of approximately 3% % for C 11 and C44, 44 , and a relative error of approximately 10% % for C12. 12 . Our study confirms fi rms the accuracy and reliability of the modified fi ed SSR model and AP-DE-QPSO algorithm in inverting the elastic constants of single crystals. [doi:10.2320/***-M2024049] / ***-M2024049]

关键词： ultrasonic backscatter measurement polycrystals single crystal elastic constants homogenization techniques intelligent optimization algorithm

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A multi-stage forecasting system for daily ocean tidal energy based on secondary decomposition, optimized gate recurrent unit and error correction

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JOURNAL OF CLEANER PRODUCTION 2024年 449卷

作者： Yang, Hong Wu, Qingsong Li, Guohui Xian Univ Posts & Telecommun Sch Elect Engn Xian 710121 Shaanxi Peoples R China

Tidal energy, as a new energy, has very high research potential and practical application value. For the characteristics of tidal energy such as nonstationarity and nonlinearity, a multi -stage forecasting system for daily ocean tidal energy is proposed. It is based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), refined composite multi -scale dispersion entropy (RCMDE), empirical mode decomposition based on time -varying filter modified by white shark optimizer (WSOTVFEMD), improved gate recurrent unit using parasitic salp swarm algorithm based on differential evolution (PDESSAGRU) and error correction using CNN (CNN -EC), named as ICEEMDAN-RCMDE-WSOTVFEMD-PDESSAGRU - CNN - EC. Firstly, decompose tidal energy into a series of intrinsic mode functions (IMFs) by ICEEMDAN, and divide IMFs into high -complexity and low -complexity components by RCMDE. Next, secondly decompose the reconstructed highcomplexity components into high -complexity parts by WSOTVFEMD. Afterwards, separately predict each component of the high -complexity parts and the low -complexity components by PDESSA, and reconstruct the predicted results to obtain original predicting results. In the end, decompose the error into error IMFs (EIMFs) by ICEEMDAN, predict EIMFs with convolutional neural network (CNN) respectively to acquire error predicting results, and reconstruct original predicting results and error predicting results to acquire the final results. Taking the tidal energy of the American cities including San Francisco, Sitka, and Wauna as case studies, the results show that the proposed system has high prediction accuracy after experiments with 13 comparative models in each city.

关键词： Tidal energy Predict Mode decomposition intelligent optimization algorithm Forecasting Error correction

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Feature Variable Selection for Near-Infrared Spectroscopy Based on Simulated Annealing Bee Colony algorithm

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JOURNAL OF CHEMOMETRICS 2025年第1期39卷

作者： Shi, Jianfei Tong, Baihong Liu, Jinming Chen, Zhengguang Li, Pengfei Tan, Chong Heilongjiang Bayi Agr Univ Coll Informat & Elect Engn Daqing Peoples R China Heilongjiang Acad Black Soil Conservat & Utilizat Harbin Peoples R China Harbin Univ Commerce Sch Food Engn Harbin Peoples R China

Variable selection is an effective method to enhance the modeling performance of near-infrared spectroscopy. Given the promising application prospects of intelligent optimization algorithms in spectral feature variable selection, this article combines the artificial bee colony algorithm with the simulated annealing algorithm to construct a simulated annealing bee colony algorithm (SABC). To explore the feasibility of SABC for spectral variable selection, SABC was applied to construct a partial least squares spectral quantitative detection model for corn stover cellulose and soil organic matter contents. The modeling performance was compared with that of the full spectrum, genetic algorithm, simulated annealing algorithm, and artificial bee colony algorithm;it was found that the model regression precision established by SABC was the best. For the cellulose and organic matter content detection models, the coefficients of determination of the validation set were 0.9433 and 0.9853, with the relative root mean squared error of 1.7901% and 0.8011%, and the residual prediction deviation of 4.1741 and 8.3931, respectively, which could meet the corresponding actual detection needs. SABC adopted the strategy of multiple runs to select the repeated wavelength variables, effectively reduced variable dimensions and model complexity, improved the prediction performance of the regression model, and provided a new approach for building a high-performance near-infrared spectroscopy (NIRS) quantitative calibration model.

关键词： feature variable selection intelligent optimization algorithm near-infrared spectroscopy partial least squares simulated annealing bee colony algorithm

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Prediction Model for Complex Process Based on Adaptive Neural Fuzzy Inference System Optimized by Swarm Intelligence optimization algorithms

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ENGINEERING LETTERS 2025年第1期33卷 48-58页

作者： Yan, Bing Xing, Cheng Wang, Jie-Sheng Shang-Guan, Yi-Peng Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China

Adaptive Neural Fuzzy Inference System (ANFIS) is a new type of fuzzy inference system structure that organically combines fuzzy logic and neuronal network, and the typical ANFIS adopts raster partitioning which is the most commonly used method of input space partitioning, and hybrid algorithm combining back propagation algorithm and least squares to adjust the premise parameter and the conclusion parameter, and to generate the If-Then rule automatically. However, since it is difficult to tune the parameters to the global optinium by the traditional ANFIS, a prediction model for a complex process adaptive neural fuzzy inference system based on a swarm intelligent optimization algorithm is proposed. Firstly, the data set is divided by FCM clustering to get the initial affiliation function, secondly, three swarm intelligence optimization algorithms, Bee-Eater Hunting (BEH) algorithm, Harmony Searching (HS) algorithm and Particle Swarm optimization (PSO) algorithm, are used to optimize the parameters of the affiliation function of the ANFIS, and finally, the optimized parameters are fit by the least squares method and then predicted to the target variables. The prediction is carried out for the intrusion detection and penicillin fermentation dataset in wireless sensor networks, and the simulation results show that the model has good prediction accuracy, which provides a new idea for solving the optimization of the prediction model for complex processes.

关键词： Industrial process intelligent optimization algorithm ANFIS: Prediction model Industrial process

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Orbital prediction accuracy improvement method based on particle swarm optimization RBF neural network

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ADVANCES IN SPACE RESEARCH 2025年第11期75卷 8105-8121页

作者： Guo, Muyu Lu, Taiyang Chen, Shibo Song, Xiangshuai Liu, Dakai Wu, Xiande Harbin Engn Univ Coll Aerosp & Civil Engn Harbin 150001 Peoples R China

In response to the increasing demand for the improved accuracy in satellite orbit prediction, this study introduces a method to enhance the orbit prediction precision with RBF neural network utilizing particle swarm optimization. The research specifically targets low-Earth orbit satellites for the development and modeling process. The constructed PSO-RBFNN model is trained using the orbital error data from real satellites, enabling the prediction of future satellite error data. Then, the prediction error data is utilized to correct any errors that may arise during the calculation of orbit predictions in the dynamics model. In order to demonstrate the outstanding performance of the model proposed in this paper, we trained the PSO-RBFNN designed in this study and other common neural network models (such as BP, LSTM, and RBF) on data at different time intervals. The results confirm that the PSO-RBFNN has good prediction ability when applied to orbital error data. Furthermore, the study examines the impact of neural network architecture and training data on the performance of the model. Finally, this study evaluates the generalization capability of a model designed on one satellite to other satellites. (c) 2025 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Broadcast ephemeris Orbit prediction intelligent optimization algorithm Neural network

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A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

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Computers, Materials & Continua 2024年第4期79卷 19-46页

作者： Tsu-Yang Wu Haonan Li Saru Kumari Chien-Ming Chen School of Artificial Intelligence(School of Future Technology) Nanjing University of Information Science&TechnologyNanjing210044China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Mathematics Chaudhary Charan Singh UniversityMeerutUttar Pradesh250004India

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant *** response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law algorithm(AFLA-SCNN)is *** Adaptive Fick’s Law algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update *** adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the *** helps the algorithm avoid falling into local optimal solutions and improves the searchability of the *** probability update strategy helps to improve the exploitability and adaptability of the *** the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal ***’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for *** results indicate AFLA’s marked performance superiority over nine other prominent optimization ***,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity *** experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia *** them,the Accuracy of the AFLA-SCNN model on India

关键词： Adaptive Fick’s law algorithm spectral convolutional neural network metaheuristic algorithm intelligent optimization algorithm hyperspectral image classification

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In-field calibration of triaxial accelerometer based on PE-ANGO

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TM-TECHNISCHES MESSEN 2024年第2期91卷 86-101页

作者： Qiao, Meiying Yao, Wenhao Gao, Kefei Du, Heng Zhao, Kaidong Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo 454003 Peoples R China

This paper presents a new method for in-field calibration of accelerometers to address the problems of low efficiency and high cost associated with traditional calibration methods. A nonlinear mathematical model of the accelerometer is established, and the cost function is analysed and deduced the cost function. Then, an adaptive Northern Goshawk Optimisation (NGO) algorithm based on prior knowledge enhancement is introduced. A method of collecting multi-position data with a hand-held accelerometer is introduced, and the proposed algorithm is used to in-field calibrate nine parameters of the accelerometer's nonlinear error model. In addition, simulation is used to compare the results of calibrating the accelerometer using the proposed algorithm and the original algorithm, demonstrating the superiority of the proposed algorithm. Finally, experimental results confirm that the proposed method can rapidly calibrate accelerometer error parameters without relying on complex equipment and with greater accuracy than traditional methods.

关键词： calibration Inertial Measurement Unit three-axis accelerometer intelligent optimization algorithm Northern Goshawk optimization algorithm

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Stepwise Multi-Objective Parameter optimization Design of LLC Resonant DC-DC Converter

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ENERGIES 2024年第8期17卷 1919-1919页

作者： Yin, Miaomiao Luo, Quanming Chongqing Univ Sch Elect Engn State Key Lab Power Transmiss Equipment Technol Chongqing 400044 Peoples R China

The LLC resonant converter, which is extensively utilized across various industrial fields, significantly depends on its parameters for performance optimization. This paper establishes a time-domain analytical model for the LLC resonant converter under Pulse Frequency Modulation (PFM) and proposes a multi-objective parameter optimization design method with stepwise constraints. The proposed method limits the resonant capacitor voltage while ensuring that the converter meets the voltage gain requirement and realizes Zero-Voltage Switching (ZVS). The converter's performance is then optimized with the objective of minimizing the switching frequency range, the resonant inductor current, and the RMS value of the switching current on the secondary side. Compared with the existing methods, the proposed method has the advantages of comprehensive consideration and wide application scenarios. Finally, a 1200 W experimental prototype was fabricated, with experimental results verifying the feasibility of the proposed optimization design method and demonstrating that the prototype's maximum efficiency reaches 96.54%.

关键词： DC/DC intelligent optimization algorithm LLC resonant converter parameter design

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