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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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Performance-oriented design of thermal-activated building envelope based on multi-objective optimization: Method and application

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ENERGY AND BUILDINGS 2025年 328卷

作者： Zhao, Tian Zhuang, Zhi Chang, Tianxin Grunewald, John Yu, Yao Tongji Univ Siping Rd 1239 Shanghai 200092 Peoples R China Tech Univ Dresden Inst Bldg Climatol Zellescher Weg 17 D-01069 Dresden Germany North Dakota State Univ Dept Civil Construct & Environm Engn Fargo ND USA

The thermal-activated building envelope (TABE) offers potential for zero-energy buildings by leveraging lowgrade natural energy and dynamically adapting to environmental changes. This study proposed a systematic performance-oriented design method for TABEs based on multi-objective optimization theory, mainly consisting of six parts: objective function, design variable, physical model, operation strategy, optimization algorithm, and evaluation decision. In addition, a typical TABE with a capillary network was selected for case study to demonstrate the effectiveness of this approach. By applying the proposed design method, optimal TABE configurations with capillary networks embedded in the inner plaster layer were obtained. Results show that multiobjective optimization effectively balances various objectives: maximizing annual thermal comfort hours, minimizing the annual heating and cooling loads, optimizing material costs, minimizing embodied carbon emissions, etc. Compared to single-objective optimization, the multi-objective approach achieves a superior balance across multiple criteria. For instance, a design solely optimized for maximizing annual thermal comfort hours or minimizing annual heating and cooling loads resulted in more than twice the material costs and embodied carbon emissions of those obtained through multi-objective optimization. Similarly, single-objective designs targeting lower material costs or embodied carbon emissions underperformed in energy efficiency. Compared to traditional ultra-low energy building envelopes, TABE offers enhanced energy efficiency, and provides at least 26.89% more comfort hours, 57.77% lower material costs, and nearly half the embodied carbon emissions. These results underscore that the proposed performance-oriented design method significantly enhances the overall functionality of TABEs, providing a robust approach for achieving sustainable and balanced building design.

关键词： Thermal-activated building envelope Performance-oriented design Multi-objective optimization intelligent optimization algorithm Operation strategy

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Walking load model considering damping and energy compensation strategy

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NONLINEAR DYNAMICS 2025年第8期113卷 7803-7825页

作者： Cao, Liang Zhou, Hailei Chen, Y. Frank Ren, Zhichao Liu, Jiepeng Tian, Qin 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 Penn State Univ Dept Civil Engn Middletown PA 17055 USA Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China Nanchang Univ Sch Infrastruct Engn Nanchang 330031 Peoples R China

This paper introduces an improved walking load model contemplating the influence of damping. The model employs the Hunt-Crossley model to guarantee the ground reaction forces generated when the support leg interfaces with the ground is zero. The model's energetic compensation is realized by adjusting the leg's rigidity. Lagrange's equation is utilized to establish the equations of motion. To verify the accuracy of the proposed model, an intelligent optimization algorithm is employed to identify the initial parameters. The model simulated ground reaction forces are compared well with the measured ones. The model parameters are compared between the proposed ant-lion optimization algorithm and three other walking load models for simulating pedestrian walking loads. The parametric analysis reveals that: the walking duration increases with increasing roller radius and decreases with increasing walking speed and leg stiffness, and the peak ground reaction forces increase with increasing leg stiffness, damping ratio, walking speed, and impact angle and decrease with increasing roller radius. The proposed model is suitable for accurate prediction of the ground reaction forces induced from human walking and is a good way for simulating human-induced vibrations.

关键词： Walking load model intelligent optimization algorithm Variable damping Energy compensation strategy

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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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Calculation and regulation of mutual inductance of wireless charging coils with metal shielding materials based on multiple intelligent optimization algorithms

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INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS 2024年第10期52卷 5166-5183页

作者： Wang, Jia hui Lin, Ling yan Zhang, Wen jie Kang, Ai liang Xu, Chun yu Tian, Jia chen Taiyuan Univ Technol Natl & Prov Joint Engn Lab Intelligent Elect Appar Taiyuan Peoples R China Taiyuan Univ Technol TYUT Elect & Commun Engn Taiyuan Shanxi Peoples R China

In wireless charging systems for electric vehicles, fluctuations in coil mutual inductance can affect both system output power and efficiency. This paper introduces a coil mutual inductance model that accounts for metal shielding material. The formula for coil mutual inductance is derived using Maxwell's equations and boundary conditions. Additionally, the paper presents a tactic for adapting the mutual inductance with the aid of an intelligent optimization algorithm, which is utilized for steadying the mutual inductance of the transmission coil. Lastly, a model of the transmission coil is developed to authenticate the practicality of the mutual inductance equation and the adaptation scheme. Measurement and calculation results indicate that the difference between the calculated value of the mutual inductance formula and the actual mutual inductance value of the coil, following deflection and offset, is below 5%. Moreover, the algorithm, along with the employed adaptation strategy, consistently demonstrates effectiveness in maintaining the mutual inductance value within a predefined range. The temporal efficiency of the algorithm, as measured by the time taken to determine this coordinate position, is consistently under 1 s. This ensures that the algorithm exhibits a rapid response time, a crucial attribute in ensuring its practical utility. We derived the formula for calculating the mutual inductance value of a circular coil with a double-layer metal shielding material and designed a fast calculation and adjustment strategy for the mutual inductance value of the coil using an intelligent optimization algorithm. image

关键词： coil mutual inductance intelligent optimization algorithm metal materials parameter design wireless charging system

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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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Research on optimization of CCUS Injection Production Parameters in High-Temperature Reservoirs Based on intelligent optimization algorithms

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第6期49卷 8919-8937页

作者： Wang, Guodong Hou, Zhiwei Shi, Li PetroChina Liaohe Oilfield Explorat & Dev Res Inst Panjin Liaoning Peoples R China

The paper takes the Jidong Nanbu high temperature oil reservoir as the research object and establishes the comprehensive numerical model of CO2 storage and displacement based on the CO2 displacement mechanism, CO2 heat recovery mechanism, and CO2 geological storage mechanism. On this basis, the injection and production optimization model is established by combining the theory of multi-objective and single-objective optimization algorithm respectively. The objective function of the multi-objective injection and production optimization model is NPV and CO2 heat recovery. The model is solved using the NSGAII and MOPSO algorithm. The results show that the objective function value obtained by the MOPSO algorithm is better than the NSGAII algorithm. The optimal values of NPV and CO2 heat recovery were 4.781 x 108 CNY and 1.078 x 1016 J respectively. The objective function of the single objective injection and production optimization model is the NPV. GA and DE optimization algorithms are used to solve the model. The results show that the DE algorithm gets the better objective function value than the GA algorithm. The optimal value of NPV is 3.69 x 1010 CNY.

关键词： High temperature reservoir CCUS Injection-production parameters intelligent optimization algorithm Multi-objective optimization Single-objective optimization

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Parameter estimation of fractional-order system with improved Archimedes optimization algorithm

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INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2025年第3期36卷

作者： Chen, Yinbin Yang, Renhuan Yang, Xiuzeng Yang, Renyu Huang, Qidong Chen, Guilian Zhang, Ling Wei, Mengyu Zhou, Yongqiang Technol Jinan Univ Coll Informat Sci Guangzhou 510632 Peoples R China Guangxi Normal Univ Nationalities Dept Phys & Elect Engn Chongzuo 532200 Peoples R China Guangdong Univ Finance Econ Sch Informat Sci Guangzhou 510320 Peoples R China Guangzhou Vocat Coll Technol & Business Expt & Training Ctr Guangzhou 510632 Peoples R China Univ Macau Fac Sci & Technol Macau 999078 Peoples R China Wuyi Univ Sch Elect & Informat Engn Jiangmen 550001 Peoples R China

In this paper, aiming at the problems of slow estimation speed and low estimation precision of traditional fractional-order system (FOS) parameter estimation method, an improved Archimedes optimization algorithm (IAOA) is proposed to calculate the optimal value. By establishing the parameter estimation model and the cost function, the parameter estimation problem is formulated as an optimization problem. As opposed to the Archimedes optimization algorithm (AOA), the IAOA introduces three improvements: leadership behavior, levy flight behavior and a new adaptive strategy. This paper verifies the performance of the IAOA by selecting 10 classic test functions. IAOA is applied to the parameter estimation problem of fractional-order unified system to verify the accuracy and feasibility of the algorithm. The simulation results prove that the IAOA has better global optimization ability and estimation accuracy than the original algorithm.

关键词： Parameter estimation fractional-order system improved Archimedes optimization algorithm intelligent optimization algorithm

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Considering both energy effectiveness and flight safety in UAV trajectory planning for intelligent logistics

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VEHICULAR COMMUNICATIONS 2025年 52卷

作者： Liu, Zhiyang Li, Liuhuan Zhang, Xiao Tang, Wan Yang, Zhen Yang, Ximin South Cent Minzu Univ Coll Comp Sci Wuhan 430074 Peoples R China State Ethn Affairs Commiss Key Lab Cyber Phys Fus Intelligent Comp Wuhan 430074 Peoples R China

In low-altitude economic logistics scenarios, trajectory planning for unmanned aerial vehicles (UAVs) can be treated as a typical traveling salesman problem (TSP). High-rise buildings in urban areas not only severely impact the flight safety of UAVs, but also increase their energy consumption when avoiding obstacles, thereby affecting their delivery ranges. To address these issues, this paper proposes a two-stage trajectory planning solution called ACO-DQN-TP for logistics UAVs. In the first stage, the ant colony optimization (ACO) algorithm is applied to solve the sequence for multi-target point deliveries, to obtain the optimal flight paths. The ant tabu table is reopened to allow for retracing of the movement paths in order to avoid forward search dilemmas. In the second stage, a deep Q-network (DQN) is combined with the traditional artificial potential field method to enhance the interaction between UAVs and their environment. The rewards are accumulated using two potential functions generated based on the target points and obstacles, to minimize the changes in the yaw angles and smooth the flight trajectory of the UAV. Simulation experiments were conducted on UAV trajectory planning for delivery missions with four to ten target points. The simulation results show that the average path length obtained by ACO-DQN-TP is 65% and 79% shorter than that of Greedy +DQNPF and BACO, respectively, and the sum of turning angles along the path is 56% of Greedy DQNPF and 72% of BACO on average. It indicates that the proposed ACO-DQN-TP scheme not only optimizes delivery routes compared to traditional ACOs but also effectively controls the magnitude of the changes in heading angle during flight. This ensures flight safety for the UAV through obstacle avoidance while reducing flight energy consumption. In particular, the heading angle optimization mechanism proposed in this paper has universal guiding significance for low-altitude flights in the areas of traffic and transporta

关键词： intelligent logistics UAV trajectory planning intelligent optimization algorithm Deep Q-network Artificial potential field

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