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Power optimization of gas pipelines via an improved particle swarm optimization algorithm

Petroleum Science 2012年第1期9卷 89-92页

作者： Zheng Zhiwei Wu Changchun Beijing Key Laboratory of Urban Oil and Gas Distribution Technology China University of PetroleumBeijing 102249China

In past decades dynamic programming, genetic algorithms, ant colony optimization algorithms and some gradient algorithms have been applied to power optimization of gas pipelines. In this paper a power optimization model for gas pipelines is developed and an improved particle swarm optimization algorithm is applied. Based on the testing of the parameters involved in the algorithm which need to be defined artificially, the values of these parameters have been recommended which can make the algorithm reach efficiently the approximate optimum solution with required accuracy. Some examples have shown that the relative error of the particle swarm optimization over ant colony optimization and dynamic programming is less than 1% and the computation time is much less than that of ant colony optimization and dynamic programming.

关键词： Gas pipeline operation optimization particle swarm optimization algorithm

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New method for prediction and solving the problem of drilling fluid loss using modular neural network and particle swarm optimization algorithm

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2014年第4期4卷 371-379页

作者： Toreifi, Hojjat Rostami, Habib Manshad, Abbas Khaksar Persian Gulf Univ Sch Engn Oil Engn Dept Bushehr Iran Persian Gulf Univ Sch Engn Comp Engn Dept Bushehr Iran Petr Univ Technol Abadan Fac Petr Engn Dept Petr Engn Abadan Iran

Loss circulation is a common problem in drilling industry that causes high expenditure on drilling companies. Nowadays minimizing of loss circulation is a main goal and preference for drilling engineers. Artificial intelligence (Al) is a new method of solving engineering problems that has the ability to consider all effective parameters simultaneously. Moreover, it has generalization and the ability to learn directly from field data. In this paper, two models were designed using Al and data of 38 wells located in Maroun oil field. Both models were developed by modular neural network, to predict loss circulation in quality and quantity. Then, the particle swarm optimization algorithm was used to minimize loss circulation. The accuracy of two models in predicting loss circulation quantitatively and qualitatively is 0.94 and 0.98 %, respectively.

关键词： Loss circulation Modular neural network Loss circulation reduction particle swarm optimization algorithm

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Implementation of genetic and particle swarm optimization algorithm for voltage profile improvement and loss reduction using capacitors in 132 kV Manipur transmission system

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ENERGY REPORTS 2023年 9卷 738-746页

作者： Bidyanath, Khaidem Singh, Sanasam Dhanabanta Adhikari, Shuma NIT Manipur Imphal 795004 Manipur India

Complex power plays a key role in maintaining and sustaining the fields i.e. magnetic and electric fields. The efficiency of the power system depends entirely on the loss and voltage profile level of the system. The lower the level of loss and the higher the voltage profile, the higher the system efficiency. Installation of the capacitors on a system greatly helps in controlling and reducing levels of the reactive energy of the system. The location and size of the capacitors that need to be installed are of great importance in the installation of the bank of capacitors. The idea of a Genetic algorithm and also a particle swarm algorithm are deployed in this paper for calculating the optimal location and size of the capacitor. In this paper, the system of the 132 KV Manipur Transmission System is designed in ETAP considering a radial distribution part and the optimization method of a genetic method, and also the particle swarm algorithm is utilized for finding the optimal location and size of the capacitor. The system is further optimized by reducing the loss and improving the voltage profile of the system. And the comparison of both the optimization techniques is presented. The results show that PSO provides optimal solutions while GA is simpler in the optimization process. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Etap Matlab Reactive power Genetic algorithm particle swarm optimization algorithm Optimal capacitor placement Power loss

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Design of Zero-Differential Steering Controller for Tracked Vehicles With Hydraulic-Mechanical Transmission Based on particle swarm optimization algorithm

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IEEE ACCESS 2023年 11卷 32187-32200页

作者： Jia, Weijian Liu, Xixia Zhang, Chuanqing Qiu, Mianhao Tan, Yongying Yu, Zhang Army Acad Armored Forces Beijing 100072 Peoples R China Beijing Special Engn & Design Inst Beijing 100028 Peoples R China

The handling stability of tracked vehicles not only affects the handling convenience of drivers but is also an important part of vehicle active safety. A zero-differential steering controller for tracked vehicles with hydraulic-mechanical transmissions was designed in this paper. First, the working principle of the steer-by-wire systems of tracked vehicles was analyzed, and the vehicle speed calculation model was established. Then, the steering dynamics model of the tracked vehicle was established based on the shear stress model. Finally, based on the particle swarm optimization algorithm, the established tracked vehicle steering dynamics model was iteratively solved, and the optimal yaw rate gain K-r was calculated in real time and used for vehicle steering control. The steering control simulation model of tracked vehicles was established in MATLAB/Simulink, and the control effect of the designed steering controller was verified by the simulation. The control effect was evaluated by the comprehensive evaluation index of the handling stability. The simulation results showed that the steering controller based on the particle swarm optimization algorithm effectively improved the handling stability of the tracked vehicle and reduced the burden on the driver.

关键词： Mathematical models Stability criteria Wheels Vehicles Hydraulic systems Gears Engines particle swarm optimization algorithm steer-by-wire system tracked vehicle zero-differential steering controller

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Seepage safety monitoring model for an earth rock dam under influence of high-impact typhoons based on particle swarm optimization algorithm

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Water Science and Engineering 2017年第1期10卷 70-77页

作者： Yan Xiang Shu-yan Fu Kai Zhu Hui Yuan Zhi-yuan Fang Dam Safety Management Center of the Ministry of Water Resources Nanjing Hydraulic Research InstituteNanjing 210029China State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Nanjing Hydraulic Research InstituteNanjing 210029China College of Mechanics and Materials Hohai UniversityNanjing 210098China School of Water Resources and Hydraulic Engineering Yunnan Agricultural UniversityKunming 650000China

Extreme hydrological events induced by typhoons in reservoir areas have presented severe challenges to the safe operation of hydraulic structures. Based on analysis of the seepage characteristics of an earth rock dam, a novel seepage safety monitoring model was constructed in this study. The nonlinear influence processes of the antecedent reservoir water level and rainfall were assumed to follow normal distributions. The particle swarm optimization （PSO） algorithm was used to optimize the model parameters so as to raise the fitting accuracy. In addition, a mutation factor was introduced to simulate the sudden increase in the piezometric level induced by short-duration heavy rainfall and the possible historical extreme reservoir water level during a typhoon. In order to verify the efficacy of this model, the earth rock dam of the Siminghu Reservoir was used as an example. The piezometric level at the SW1-2 measuring point during Typhoon Fitow in 2013 was fitted with the present model, and a corresponding theoretical expression was established. Comparison of fitting results of the piezometric level obtained from the present statistical model and traditional statistical model with monitored values during the typhoon shows that the present model has a higher fitting accuracy and can simulate the uprush feature of the seepage pressure during the typhoon perfectly.

关键词： Monitoring model particle swarm optimization algorithm Earth rock dam Lagging effect Typhoon Seepage pressure Mutation factor Piezometric level

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Evaluation of multi-objective inverse heat conduction problem based on particle swarm optimization algorithm, normal distribution and finite element method

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2018年 127卷 1114-1127页

作者： Wang, Xiaowei Li, Huiping He, Lianfang Li, Zhichao Shandong Univ Sci & Technol Sch Mat Sci & Engn 579 Qianwangang Rd Qingdao 266590 Shandong Peoples R China

The boundary heat transfer coefficient (BHTC) and thermal conductivity are the most essential thermal physical parameters, which have a significant effect on the calculation accuracy of physical fields in the numerical simulation. To simultaneously estimate the temperature-dependent BHTC and thermal conductivity by solving the inverse heat conduction problem (IHCP), a hybrid method (ZPSO) based on particle swarm optimization algorithm (PSO), normal distribution method and finite element method (FEM) is presented, where normal distribution method representing the transient solution space is used to improve the convergence speed. A two-dimensional direct heat conduction problem (DHCP) with the temperature-dependent thermal-physical parameters is simulated based on FEM. Some temperature curves at certain position of FEM model are attained in the simulation. The temperature-dependent BHTC and thermal conductivity are simultaneously evaluated by PSO-FEM and ZPSO-FEM according to the temperature curves attained in DHCP. Comparing the temperature-dependent thermal-physical parameters attained in the IHCP to that used in the DHCP, the results show that the BHTC and thermal conductivity evaluated by the ZPSO are well consistent with those used in the DHCP. The evaluation results show that the convergence of the hybrid method is well, and the convergence speed is accelerated by the ZPSO. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Inverse heat conduction Multi-objective problem particle swarm optimization algorithm Finite element method

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Improved particle swarm optimization algorithm and its application in text feature selection

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APPLIED SOFT COMPUTING 2015年 35卷 629-636页

作者： Lu, Yonghe Liang, Minghui Ye, Zeyuan Cao, Lichao Sun Yat Sen Univ Sch Informat Management Guangzhou 510275 Guangdong Peoples R China

Text feature selection is an importance step in text classification and directly affects the classification performance. Classic feature selection methods mainly include document frequency (DF), information gain (IG), mutual information (MI), chi-square test (CHI). Theoretically, these methods are difficult to get improvement due to the deficiency of their mathematical models. In order to further improve effect of feature selection, many researches try to add intelligent optimization algorithms into feature selection method, such as improved ant colony algorithm and genetic algorithms, etc. Compared to the ant colony algorithm and genetic algorithms, particle swarm optimization algorithm (PSO) is simpler to implement and can find the optimal point quickly. Thus, this paper attempt to improve the effect of text feature selection through PSO. By analyzing current achievements of improved PSO and characteristic of classic feature selection methods, we have done many explorations in this paper. Above all, we selected the common PSO model, the two improved PSO models based respectively on functional inertia weight and constant constriction factor to optimize feature selection methods. Afterwards, according to constant constriction factor, we constructed a new functional constriction factor and added it into traditional PSO model. Finally, we proposed two improved PSO models based on both functional constriction factor and functional inertia weight, they are respectively the synchronously improved PSO model and the asynchronously improved PSO model. In our experiments, CHI was selected as the basic feature selection method. We improved CHI through using the six PSO models mentioned above. The experiment results and significance tests show that the asynchronously improved PSO model is the best one among all models both in the effect of text classification and in the stability of different dimensions. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Text classification Text feature selection particle swarm optimization algorithm Constriction factor

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Sequence Transfer-Based particle swarm optimization algorithm for Irregular Packing Problems

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IEEE ACCESS 2021年 9卷 131223-131235页

作者： Fang, Jie Rao, Yunqing Liu, Pan Zhao, Xusheng Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

The two-dimensional (2D) irregular packing problem is a classical optimization problem with NP-hard characteristics and has high computational complexity. To date, packing problems have generally been solved by artificial experience and heuristic algorithms. However, these algorithms are not highly efficient and the excellent cases cannot be preserved, which both time and economic costs are high. Inspire by transfer learning and considering the characteristics of 2D irregular packing problems, we propose a sequence transfer-based particle swarm optimization algorithm (ST-PSO) to solve the multi-constraint packing problem. A piece-matching strategy based on an improved shape context algorithm, and a piece-sequencing generation strategy for transferring the packing sequence are developed for particle swarm optimization(PSO) initialization. In the process of PSO, an adaptive adjustment strategy is used with an improved positioning strategy to adjust the packing position of the pieces. The results indicate that this method can robustly, quickly, and efficiently achieve the packing of 2D irregular pieces. Compared with the data prior to transfer, the ST-PSO can inherit and transfer the historical packing sequence in less time and retain or exceed the actual packing data onto the samples. This algorithm could be applied to industrial applications to reduce waste, packing time, and production costs.

关键词： Heuristic algorithms Task analysis Licenses Transfer learning Shape Feature extraction optimization Irregular pieces multiple constraints particle swarm optimization algorithm sequence transfer two-dimensional packing optimization

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Inverse calculation of vessel emission source intensity based on optimized Gaussian puff model and particle swarm optimization algorithm

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MARINE POLLUTION BULLETIN 2024年第PT A期209卷 117117页

作者： Wu, Hao Li, Xueyao Wang, Chao Ye, Zhirui Jinling Inst Technol Sch Network &Commun Engn Nanjing Peoples R China Wuhan Univ Technol Hubei Key Lab Inland Shipping Technol Wuhan Hubei Peoples R China Southeast Univ Sch Transportat Nanjing Peoples R China Southeast Univ Sch Econ & Management Nanjing Peoples R China

Maritime transportation has significantly contributed to global economic development but is also a major source of air pollution. This study aims to provide an inverse calculation framework of vessel emission source intensity for emission monitoring. This research enhanced the traditional Gaussian diffusion model by specific characteristics of ship emissions and various influencing factors identified through simulation experiments. An inverse model is developed using pattern search and particle swarm optimization (PSO) algorithms to estimate marine exhaust source strengths. Results indicate that the PSO algorithm is the most accurate and efficient, especially with an iteration step size of 0.1 s. Practical application using data from 86 monitored ships revealed that 76 had fuel sulfur content exceeding the 0.1 % threshold, achieving an accuracy rate of 88.37 %. These findings are crucial for improving the understanding of marine exhaust dispersion and advancing remote monitoring technologies, contributing to better environmental management of maritime transport emissions.

关键词： Gaussian model Inverse calculation particle swarm optimization algorithm Vessel emission

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Customer Segmentation Using K-Means Clustering and the Hybrid particle swarm optimization algorithm

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COMPUTER JOURNAL 2023年第4期66卷 941-962页

作者： Li, Yue Qi, Jianfang Chu, Xiaoquan Mu, Weisong China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Minist Agr Key Lab Viticulture & Enol Beijing 100083 Peoples R China

In a competitive market, it is of great significance to divide customer groups to develop customer-centered personalized products. In this paper, we propose a customer segmentation method based on the K-means algorithm and the improved particle swarm optimization (PSO) algorithm. As the PSO algorithm easily falls into local extremum, the improved hybrid particle swarm optimization (IHPSO) algorithm is proposed to improve optimization accuracy. The full factorial design is used to determine the optimal parameter combination;the roulette operator is used to select excellent particles;then, the selected particles are crossed according to their adaptive crossover probabilities;when the population falls into a local optimum, the particles are mutated according to their adaptive mutation probabilities. Aimed at the K-means' sensitivity to selecting the initial cluster centers, IHPSO is used to optimize the cluster centers (IHPSO-KM). We compare IHPSO with the PSO, LDWPSO, GA, GA-PSO and ALPSO algorithms on nine benchmark functions. We also conduct comparative experiments to compare IHPSO-KM with several conventional and state-of-the-art approaches on five UCI datasets. All results show that the two proposed methods outperform existing models. Finally, IHPSO-KM is applied in customer segmentation. The experimental results also prove the rationality and applicability of IHPSO-KM for customer segmentation.

关键词： K-means clustering algorithm particle swarm optimization algorithm hybrid mechanism cluster analysis customer segmentation

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