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Research on an optimization Method for Injection-Production Parameters Based on an Improved particle swarm optimization algorithm

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ENERGIES 2022年第8期15卷 2889页

作者： Dong, Yukun Zhang, Yu Liu, Fubin Zhu, Zhengjun China Univ Petr Coll Comp Sci & Technol Qingdao 266580 Peoples R China PetroChina Tarim Oilfield Co Res Inst Explorat & Dev Korla 841000 Peoples R China

The optimization of injection-production parameters is an important step in the design of gas injection development schemes, but there are many influencing factors and they are difficult to determine. To solve this problem, this paper optimizes injection-production parameters by combining an improved particle swarm optimization algorithm to study the relationship between injection-production parameters and the net present value. In the process of injection-production parameter optimization, the particle swarm optimization algorithm has shortcomings, such as being prone to fall into local extreme points and slow in convergence speed. Curve adaptive and simulated annealing particle swarm optimization algorithms are proposed to further improve the optimization ability of the particle swarm optimization algorithm. Taking the Tarim oil field as an example, in different stages, the production time, injection volume and flowing bottom hole pressure were used as input variables, and the optimal net present value was taken as the goal. The injection-production parameters were optimized by improving the particle swarm optimization algorithm. Compared with the particle swarm algorithm, the net present value of the improved scheme was increased by about 3.3%.

关键词： particle swarm optimization algorithm injection-production optimization curve adaptation

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Accelerating reservoir production optimization by combining reservoir engineering method with particle swarm optimization algorithm

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年第PartD期208卷 109692-109692页

作者： An, Zhibin Zhou, Kang Hou, Jian Wu, Dejun Pan, Yuping China Univ Petr East China Key Lab Unconvent Oil & Gas Dev Minist Educ Qingdao 266580 Peoples R China Shandong Univ Sci & Technol Coll Energy & Min Engn Qingdao 266590 Peoples R China Qingdao Natl Lab Marine Sci & Technol Lab Marine Mineral Resources Qingdao 266237 Peoples R China China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China

Generally, the optimization of injection and production parameters in oilfields are carried out by using reservoir engineering method or mathematical algorithm individually, which limits the optimization efficiency and accuracy. To deal with this problem, the paper tries to improve production optimization performance by introducing reservoir engineering method into conventional particle swarm optimization (PSO) in three ways: the preprocessing result by reservoir engineering method is used respectively as population initialization, the search space constraint and the particle velocity guide item in PSO. Results show that all the three improved optimization methods can speed up the convergence rate of PSO algorithm while keeping similar convergence results at the same time. Furthermore, the use of the reservoir engineering preprocessing scheme as the search space constraint obtains the best convergence performance and reduces the iteration calculations by 24.14%, providing an effective way to reduce calculation cost for reservoir production optimization in commercial oilfields.

关键词： Reservoir production optimization Reservoir engineering method particle swarm optimization algorithm Improved optimization method

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Prediction of the splitting tensile strength of the bonding interface by combining the support vector machine with the particle swarm optimization algorithm

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ENGINEERING STRUCTURES 2021年 230卷 111696-111696页

作者： Jiang, Wei Xie, Youjun Li, Wenxu Wu, Jianxian Long, Guangcheng Cent South Univ Sch Civil Engn Changsha 410075 Peoples R China Changjiu Intercity Railway Ltd Co Nanchang 330002 Jiangxi Peoples R China

The bonding performance of the interface between two concrete layers is of great importance for China Rail Track System III (CRTS III) slab ballastless track structure. While the bonding performance is close to the distribution and amounts of defects such as the bubble or the void on the bonding interface. In this paper, combining the Support Vector Machine (SVM) with the particle swarm optimization (PSO) algorithm, named SVM-PSO algorithm is used to predict the splitting tensile strength of the bonding interface based on the distribution of defects. And the influence of different parameters in the SVM-PSO algorithm on the prediction ability is discussed. Results indicate that the relative error between the average strength value of every 100 predictions and the experimental strength value is less than 5%. The achievements will provide an effective method for predicting the splitting tensile strength of the bonding interface between two concrete layers like CRTS III slab ballastless track structure in practice.

关键词： Splitting tensile strength of the bonding interface Support vector machine particle swarm optimization algorithm Prediction CRTS III slab ballastless track structure

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optimization design of high-frequency ultrasonic transducer based on ANFIS and particle swarm optimization algorithm

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APPLIED ACOUSTICS 2022年 187卷

作者： Guo, Rong Chen, Dongdong Fei, Chunlong Li, Di Zhang, Qidong Feng, Wei Yang, Yintang Xidian Univ Sch Microelect Xian 710071 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

In this study, an optimization design method based on adaptive neuro-fuzzy inference system (ANFIS) and modified particle swarm optimization (PSO) algorithm is proposed to enhance the performance of high-frequency ultrasonic transducers (UTs). The optimization indexes include center frequency (CF) and -6 dB bandwidth (BW), and the design parameters (DPs) include thickness (T-p) and size (S-p) of piezo-electric layer, thicknesses of matching layers (Ag-epoxy and Parylene C). Based on simulated results, an ANFIS model is established to characterize the effect of DPs on the performance indexes (PIs). optimization targets are set as CF of 30 MHz and BW of 65%, the modified PSO algorithm is adopted to optimize the design parameters of high-frequency UT. The optimized T-p and S-p are 110 mu m and 5.2 mm. The optimized thicknesses of Ag-epoxy (T-AE) and Parylene C (T-pc) are 16 mu m and 20 mu m, respectively. Simulations and experiments of high-frequency UT are conducted based on the optimized DPs, the results well agree with the designed ones. The CF and the BW of UT fabricated by the developed method are 26.77(+/- 0.02) MHz and 65.11(+/- 0.06) %, while the CF and the BW of UT fabricated by the traditional method are 23.42 MHz and 51.4%. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Ultrasonic transducer High frequency optimization design Adaptive neuro-fuzzy inference system particle swarm optimization algorithm

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optimization of support vector machine parameters in modeling of Iju deposit mineralization and alteration zones using particle swarm optimization algorithm and grid search method

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COMPUTERS & GEOSCIENCES 2022年 165卷

作者： Abbaszadeh, Maliheh Soltani-Mohammadi, Saeed Ahmed, Ali Najah Univ Kashan Dept Min Engn Kashan Iran Univ Tenaga Nas Inst Energy Infrastructure IEI Coll Engn Dept Civil Engn Kajang 43000 Selangor Malaysia

The support vector classifier (SVC) is one of the most powerful machine learning algorithms. This algorithm has been accepted as an effective method in three-dimensional geological modeling. Although the model selection has a great impact on the performance of SVC algorithm, most of mining studies have neglected it and used the grid search method. Therefore, in this study, a new approach is proposed for improving the selection of SVC models. This approach uses particle swarm optimization (PSO) to determine the important parameters of SCV such as penalty and kernel parameters. The proposed approach was applied in the modeling process of the Iju porphyry copper deposit to delineate alteration and mineralization zones. The optimal penalty and kernel parameters were found to be 2(7.2) and 2(-4.75) for alteration zone, and 2(2.72) and 2(-6.23) for mineralization zone, respectively. With 97.4% and 97.01% rates of accuracy for mineralization and alteration zones, the PSO results showed reasonable performance in classification. The proposed approach had better accuracy than grid search method. Therefore, because of its better performance, the geological models were developed using the PSO method to be used as a basis for future resource evaluation.

关键词： Support vector machine Classification particle swarm optimization algorithm Grid search Model selection particle swarm optimization

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Predicting frost heave in soil-water systems using the generalized regression neural network optimized with particle swarm optimization algorithm

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COLD REGIONS SCIENCE AND TECHNOLOGY 2024年 226卷

作者： Cai, Honghong Wang, Chong Ma, Ziqiang Meng, Fanshuo Lin, Zhikun Ren, Junping Li, Shuangyang Lanzhou Univ Minist Educ China Key Lab Mech Disaster & Environm Western China Lanzhou 730000 Peoples R China Lanzhou Univ Sch Civil Engn & Mech Lanzhou 730000 Peoples R China China State Construct Engn Corp Aecom Consultants Lanzhou 730000 Peoples R China Chinese Acad Sci Northwest Inst Ecoenvironm & Resources State Key Lab Frozen Soil Engn Lanzhou 730000 Peoples R China

Frost heave poses a serious hazard to geotechnical engineering. However, conventional experimental and theoretical methods, which have limitations in accurately describing the deformation behavior of soils during frost heave, struggle due to the nonlinear and uncertain nature of the process. For this reason, the study leverages the advantages of the Generalized Regression Neural Network (GRNN) in handling nonlinear problems and small sample datasets. The structure of the GRNN model is further optimized using the particle swarm optimization algorithm (PSO) and K-fold Cross Validation (K). The input variables for the model include water content (W), W ), temperature (T), T ), dry density (rho), and plasticity index (Ip) I p ) under various working conditions. The frost heave rate (7) is considered as the output variable. Meanwhile, the model also considers the effects of both one-factor and two-factor interactions among the input variables on frost heave behaviors. Finally, a prediction model for 7 based on the K-PSO-GRNN is established. The results demonstrate that the K-PSO-GRNN model exhibits greater robustness and stability in predicting 7 compared to PSO-GRNN and GRNN (R2 2 = 0.94, MAE = 0.14), and the prediction residuals for 7 range from 0 to 0.4. Among these variables, W has the most significant influence on 7, followed by T , rho, and I p . Moreover, both rho and Ip p have significant interactions with T and have a notable impact on the soil's frost heave behavior. At high rho, the soil shows reduced sensitivity to frost heave in response to changes in T , while at high I p , the soil becomes more sensitive to frost heave with changes in T . 7 generally shows a positive correlation with W and rho, and a negative correlation with T . The aforementioned K-PSO-GRNN model can be utilized for predicting 7, which is valuable in forecasting non-uniform deformation hazards caused by frost heave and studying preventive measures.

关键词： Generalized regression neural network particle swarm optimization algorithm K -fold cross validation Frost heave

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Low-delay layout planning based on improved particle swarm optimization algorithm in 5G optical fronthaul network

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OPTICAL FIBER TECHNOLOGY 2021年 67卷

作者： Wang, Nan Liu, Jianfei Lu, Jia Zeng, Xiangye Zhao, Xinyu Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China Tianjin Key Lab Elect Mat & Devices Tianjin 300401 Peoples R China Hebei Key Lab Adv Laser Technol & Equipment Tianjin 300401 Peoples R China

The rapid development of time-sensitive applications, such as autopilot, telemedicine and industrial automation, demands more stringent requirements on end-to-end delay. Therefore, low-delay planning of 5G networks is supposed to be a crucial stage. In this paper, a low-delay layout planning based on the nonlinear decreasing inertia weight particle swarm optimization algorithm (NL-wPSO) is demonstrated. By iteratively calculating the velocity and position of each particle, the proposed algorithm can optimize the layout of AAUs in the fronthaul network. Thereby, the distance between the AAUs and edge computing server will be shortened. Compared to the basic PSO, the simulation results indicate a significant reduction on transmission delay.

关键词： 5G Layout planning Edge computing service particle swarm optimization algorithm

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A parallel particle swarm optimization algorithm based on GPU/CUDA

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APPLIED SOFT COMPUTING 2023年 144卷

作者： Zhuo, Yanhong Zhang, Tao Du, Feng Liu, Ruilin Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China Jingchu Univ Technol Sch Math & Phys Jingmen Hubei Peoples R China

Parallel computing is the main way to improve the computational efficiency of metaheuristic algorithms for solving high-dimensional, nonlinear optimization problems. Previous studies have typically only implemented local parallelism for the particle swarm optimization (PSO) algorithm. In this study, we proposed a new parallel particle swarm optimization algorithm (GPU-PSO) based on the Graphics Processing Units (GPU) and Compute Unified Device Architecture (CUDA), which uses a combination of coarse-grained parallelism and fine-grained parallelism to achieve global parallelism. In addition, we designed a data structure based on CUDA features and utilized a merged memory access mode to further improve data-parallel processing and data access efficiency. Experimental results show that the algorithm effectively reduces the solution time of PSO for solving high-dimensional, large-scale optimization problems. The speedup ratio increases with the dimensionality of the objective function, where the speedup ratio is up to 2000 times for the high-dimensional Ackley function. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： particle swarm optimization algorithm Parallel computing CUDA GPU function optimization [3] traveling salesman problem [4] wire

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An IMC-PID controller with particle swarm optimization algorithm for MSBR core power control

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NUCLEAR ENGINEERING AND DESIGN 2020年 360卷 110513-110513页

作者： Zeng, Wenjie Zhu, Weicong Hui, Tianyu Chen, Lezhi Xie, Jinsen Yu, Tao Univ South China Dept Nucl Sci & Technol Hengyang City 421001 Peoples R China

Internal Model Control (IMC) is a control strategy based on process mathematical model for controller design. The design idea of IMC is to parallelize the object model with the actual object, approximate the dynamic inversion of the model by the controller, and take the inverse of the minimum phase part of the model for a single variable system, and add it to the system. As the advantages of simple structure, intuitive design, no need for accurate object model, less on-line adjustment parameters in IMC design process, to explore the application of IMC in the field of nuclear reactor control, taking the core power control of Molten Salt Breeder Reactor (MSBR) as an example, a linear model of MSBR core system is established. The IMC technology was adopted and the IMC-PID controller with particle swarm optimization (PSO) algorithm was designed to control the core power. The core power control of liquid molten salt reactor under step reactivity disturbance and load tracking is studied. The results show that the IMC-PID controller can control the core power well.

关键词： IMC-PID controller particle swarm optimization algorithm MSBR Core power control

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On the Constellation Design of Multi-GNSS Reflectometry Mission Using the particle swarm optimization algorithm

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ATMOSPHERE 2019年第12期10卷 807-807页

作者： Han, Yi Luo, Jia Xu, Xiaohua Wuhan Univ Sch Geodesy & Geomat 129 Luoyu Rd Wuhan 430079 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Minist Educ Key Lab Geospace Environm & Geodesy 129 Luoyu Rd Wuhan 430079 Peoples R China

Due to the great success of the CYclone Global Navigation Satellite System (CYGNSS) mission, the follow-on GNSS Reflectometry (GNSS-R) missions are being planned. In the perceivable future, signal sources for GNSS-R missions can originate from multiple global navigation satellite systems (GNSSs) including Global Positioning System (GPS), Galileo, GLONASS, and BeiDou. On the other hand, to facilitate the operational capability for sensing ocean, land, and ice features globally, multi-satellite low Earth orbit (LEO) constellations with global coverage and high spatio-temporal resolutions should be considered in the design of the follow-on GNSS-R constellation. In the present study, the particle swarm optimization (PSO) algorithm was applied to seek the optimal configuration parameters of 2D-lattice flower constellations (2D-LFCs) composed of 8, 24, 60, and 120 satellites, respectively, for global GNSS-R observations, and the fitness function was defined as the length of the time for the percentage coverage of the reflection observations reaches 90% of the globe. The configuration parameters for the optimal constellations are presented, and the performances of the optimal constellations for GNSS-R observations including the visited and the revisited coverages, and the spatial and temporal distributions of the reflections were further compared. Although the results showed that all four optimized constellations could observe GNSS reflections with proper temporal and spatial distributions, we recommend the optimal 24- and 60-satellite 2D-LFCs for future GNSS-R missions, taking into account both the performance and efficiency for the deployment of the GNSS-R missions.

关键词： multi-GNSS GNSS reflectometry constellation design particle swarm optimization algorithm

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