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Solving optimization of a Mine Gas Sensor Layout Based on a Hybrid GA-DBPSO algorithm

IEEE SENSORS JOURNAL 2019年第15期19卷 6400-6409页

作者： Zhao, Dan Zhang, Hui Pan, Jingtao Liaoning Tech Univ Coll Safety Sci & Engn Fuxin 123000 Peoples R China Liaoning Tech Univ Minist Educ Key Lab Mine Thermodynam Disasters & Control Huludao 125105 Peoples R China Shenyang Res Inst China Coal Technol Engn Grp Shenyang 110016 Liaoning Peoples R China

To expand the monitoring range of the coal mine gas monitoring subsystem and achieve the timely early-warning of the local gas transfinite accident, the covering model of sensor was established. It can realize the function of using the least number of gas sensors to monitor the change of gas concentration in the whole mine. The objective function and constraint conditions of the established model were determined. A hybrid GA-DBPSO algorithm combining the genetic algorithm with the discrete binary particle swarm optimization algorithm was proposed to solve the gas sensor location set covering model. This research result was applied to investigate the gas sensor layout of Baoxin Coal Mine in China, and gas sensor layout schemes were obtained under the condition of different gas sensor shortest alarm time. The relationship between the shortest alarm time and the number of additional gas sensor was given, which can provide guidance and reference for the enterprises managers to make decisions on layout scheme of gas sensors.

关键词： Gas sensor layout optimization particle swarm optimization algorithm discrete combination optimization systems modeling

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Process parameter optimization for laser-magnetic welding based on a sample-sorted support vector regression

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JOURNAL OF INTELLIGENT MANUFACTURING 2019年第5期30卷 2217-2230页

作者： Zhang, Feng Zhou, Taotao Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China China Ship Dev & Design Ctr Wuhan 430064 Hubei Peoples R China

Magnetic field assisted laser welding (LW-MF) shows great potential in the jointing of large structures. The quality of the welding joint in LW-MF largely depends on the selection of process parameters. In this study, an integrated process parameter optimization framework is developed for magnetic field assisted laser welding. Firstly, Taguchi method is selected to generate sample points and the LW-MF experiments are carried out to obtain the bead geometrical characteristics. Secondly, a sample-sorted SVR (SS-SVR) metamodeling approach is developed to make full use of the already-acquired prediction error information for fitting the relationships between multiple process parameters and the bead geometrical characteristics. A detailed comparison between the developed SS-SVR metamodeling approach and existing SVR metamodeling approach for prediction accuracy is performed. Then, the particle swarm optimization is used to solve the process parameters optimization problem, in which the objective function values are predicted by the developed SS-SVR metamodel. Finally, verification experiment is conducted to verify the reliability of the obtained optimal process parameters. Results illustrate that the proposed integrated process parameter optimization framework is effective for obtaining the optimal process parameters and can be used in LW-MF for practical production.

关键词： Process parameters Magnetic field Laser welding Support vector regression particle swarm optimization algorithm

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Layout optimization of large-scale oil-gas gathering system based on combined optimization strategy

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NEUROCOMPUTING 2019年 332卷 159-183页

作者： Liu, Yang Chen, Shuangqing Guan, Bing Xu, Ping Northeast Petr Univ Sch Petr Engn Daqing 163318 Peoples R China

Layout optimization of large-scale oil-gas gathering system is a kind of NP-hard problem in the field of system optimization. It involves a large number of network nodes, coupled optimization variables, complex network structures and hydraulic constraints, which cause the great difficulty in constructing optimization models and solution methods. In this paper, a high-dimensional mixed integer nonlinear layout optimization mathematical model involving the pipeline network structure parameters and pipeline design parameters is established, which can be applied to large-scale oil gathering system and gas gathering system universally. A modified particle swarm optimization (MPSO) algorithm with global search ability is proposed. The convergence theorem of the stochastic optimization algorithm is established based on the Poincare cycle theory. Global convergence of MPSO algorithm is proved, and the performance of MPSO algorithm is analyzed by numerical experiments. Based on dimension reduction planning and modularization thought, the grid dissection set partition method is proposed, and the theoretical foundation and complexity of the grid dissection method are discussed. In order to reduce the dimension of the layout optimization problem, the concept of the fuzzy set of adjacent position, and a novel approach for the well-station connection mode optimization are put forward. Based on the MPSO algorithm, grid dissection set partition method and solution method by fuzzy set of adjacent position, a combined optimization strategy for layout optimization model is proposed. The reliability and practicality of the proposed layout optimization model and combined optimization strategy are verified by the successful application of a real-world large-scale oil field with 661 wells. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Layout optimization particle swarm optimization algorithm Global convergence Convergence theorem Oil-gas gathering system

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optimization design of general Type-2 fuzzy logic controllers for an uncertain Power-line inspection robot

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第2期37卷 2203-2214页

作者： Zhao, Tao Wu, Qing Li, Shengchuan Guo, Rui Dian, Songyi Jia, Hairui Sichuan Univ Coll Elect Engn & Informat Technol Chengdu Sichuan Peoples R China State Grid Liaoning Elect Power Co Ltd Elect Power Res Inst Shenyang Liaoning Peoples R China State Grid Shandong Elect Power Co Jinan Shandong Peoples R China Zhejiang Univ Finance & Econ China Acad Finance Res Hangzhou 310018 Zhejiang Peoples R China

In this paper, a general type-2 fuzzy logic controller (GT2FLC), which is optimized by the particle swarm optimization (PSO) algorithm, is applied to a power-line inspection (PLI) robot. The information fusion is used to design the GT2FLC to avoid the rule explosion. The proposed controller has the ability to deal with uncertainties when the PLI robot works on the insulated access cable. In order to compare the performance of the proposed controller with that of other controllers, the type-1 fuzzy logic controller (T1FLC) and the interval type-2 fuzzy logic controller (IT2FLC) are both optimized by the PSO to adjust the PLI robot. To show the ability of different controllers to deal with uncertainties, external disturbances and parameter perturbations are added to the PLI robot. According to simulations, the performance of the proposed controller is better than that of other controllers, and the proposed controller has better ability to deal with uncertainties.

关键词： Power-line inspection robot particle swarm optimization algorithm general type-2 fuzzy logic controller

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ADRC Based on Artificial Neural Network for a Six-rotor UAV

ADRC Based on Artificial Neural Network for a Six-rotor UAV

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第40届中国控制会议

作者： Lin Xi Yunfeng Shao Suli Zou Zhongjing Ma Beijing Institute of Technology Lvliang power supply company of Shanxi Electric Power Company

An on-line intelligent optimization method based on an artificial neural network is proposed for the parameter adjustment of the active disturbance rejection controller. And a cascaded ADRC controller including the artificial neural network attitude ADRC is investigated for trajectory tracking of the six-rotor UAV. First, establish the kinematics and dynamics model of the six-rotor, and design a cascaded active disturbance rejection controller for the six-rotor to deal with the non-linear disturbance problem in flight. Secondly, an artificial neural network is designed to optimize the parameters of the attitude ADRC controller on-line, and the particle swarm algorithm is used to set the initial value of the artificial neural network. Finally, the simulation results demonstrated that ADRC based on the artificial neural network can effectively resist the disturbances and enhance the robustness of the attitude controller and the cascade ADRC controller based on the artificial neural network can track the reference trajectory quickly and accurately.

关键词： active disturbance rejection control particle swarm optimization algorithm artificial neural network

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Multi-objective optimization model and algorithm implementation of the distributed power generation system for renewable energy in China and Russia

Unconventional Resources

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Unconventional Resources 2025年 7卷

作者： Ma, Yingkai Peoples' Friendship University of Russia Russian Federation

This study focuses on solving multi-objective optimization problems in distributed power generation systems (DPGS) for renewable energy in China and Russia, including low economic efficiency, poor environmental benefits, and insufficient system reliability. It proposes a hybrid optimization model that integrates deep learning with an improved particle swarm optimization algorithm, namely Adaptive Linear Decreasing Inertia Weight particle swarm optimization with Mutation Strategy (ALD-MPSO). By introducing a Dense Bidirectional Long Short-Term Memory with Attention Mechanism (DBI-LSTM-AM) model, which combines a Bidirectional Long Short-Term Memory (Bi-LSTM) network, Dense layers, and an Attention Mechanism (AM), the model performs time-series forecasting of energy demand. Coupled with the ALD-MPSO algorithm, the model simultaneously optimizes economic efficiency, environmental benefits, and system reliability. The study designs a renewable energy prediction and optimization model for DPGS, based on the fusion of the DBI-LSTM-AM and ALD-MPSO algorithms (DBI-LSTM-2AM-PSO). Finally, the model's performance is evaluated. Experimental results show that the proposed model achieves superior prediction accuracy (95.53 %), with an F1 score of 91.41 %, and a mean squared error (MSE) of 0.049, outperforming the benchmark algorithms. Additionally, the fitness value in MOO is reduced to 0.47, with a training time of only 25.7 s and low computational resource consumption (Center Processing Unit usage at 10.55 %). This study provides effective technical support for the intelligent management of DPGS in the renewable energy sectors of China and Russia. © 2025 The Authors

关键词： Deep learning Distributed power generation system Multi-objective optimization particle swarm optimization algorithm Renewable energy

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Prognostication of lignocellulosic biomass pyrolysis behavior using ANFIS model tuned by PSO algorithm

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FUEL 2019年 253卷 189-198页

作者： Aghbashlo, Mortaza Tabatabaei, Meisam Nadian, Mohammad Hossein Davoodnia, Vandad Soltanian, Salman Univ Tehran Coll Agr & Nat Resources Fac Agr Engn & Technol Dept Mech Engn Agr Machinery Karaj Iran Univ Teknol MARA Fac Plantat & Agrotechnol Shah Alam 40450 Selangor Malaysia Biofuel Res Team BRTeam Karaj Iran AREEO ABRII Microbial Biotechnol Dept Karaj Iran Inst Res Fundamental Sci IPM Brain Engn Res Ctr POB 19395-5531 Tehran Iran

In-depth knowledge on pyrolysis behavior of lignocellulosic biomass is pivotal for efficient design, optimization, and control of thermochemical biofuel production processes. Experimental thermogravimetric analysis (TGA) is usually employed to peruse the pyrolysis kinetics of biomass samples. In addition to that, the main constituents of biomass (i.e., cellulose, hemicellulose, lignin) as well as the process heating rate can excellently reflect its pyrolysis characteristics through modeling techniques. However, the application of statistical and phenomenological models for extremely complex and highly nonlinear phenomena like lignocellulose pyrolysis is challenging. To address this challenge, adaptive network-based fuzzy inference system (ANFIS) was consolidated with particle swarm optimization (PSO) algorithm to prognosticate the kinetic constants of lignocellulose pyrolysis. More specifically, the PSO algorithm was applied to tune membership function parameters of the ANFIS model. Three ANFIS-PSO topologies were designed and trained to estimate the kinetic constants of lignocellulose pyrolysis, i.e., energy of activation, pre-exponential coefficient, and order of reaction. The input variables of the developed models were biomass main constituents and the process heating rate. The developed models could predict the kinetic constants of lignocellulosic biomass pyrolysis with an R-2 > 0.970, an MAPE < 3.270%, and an RMSE < 5.006. The pyrolysis behaviors of three different biomass feedstocks (unseen data to the developed models) were adequately prognosticated with an R-2 > 0.91 using the developed models, further confirming their fidelity. Overall, the lignocellulose pyrolysis behavior could be reliably and accurately estimated using the trained ANFIS-PSO approaches as an alternative to the TGA measurements. In order to make practical use of the trained models, a handy freely-accessible software platform was designed using the selected ANFIS-PSO models for approximati

关键词： Adaptive network-based fuzzy inference system Lignocellulosic biomass particle swarm optimization algorithm Pyrolysis kinetics Thermogravimetric analysis

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A new NN-PSO hybrid model for forecasting Euro/Dollar exchange rate volatility

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NEURAL COMPUTING & APPLICATIONS 2019年第7期31卷 2063-2071页

作者： Hajizadeh, Ehsan Mahootchi, Masoud Esfahanipour, Akbar Massahi, Mandi Kh Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran

An accurate estimation of exchange rate return volatility is an important step in financial decision making problems. The main goal of this study is to enhance the ability of GARCH-type family models in forecasting the Euro/Dollar exchange rate volatility. For this purpose, a new neural-network-based hybrid model is developed in which a predefined number of simulated data series generated by the calibrated GARCH-type model along with other explanatory variables is used as input variables. The optimum number of these data series and other parameters of the network are tuned by an efficient particle swarm optimization algorithm. Using two datasets of real Euro/Dollar rates, how the proposed hybrid model could reasonably enhance the results of GARCH-type models and the traditional neural network in terms of different performance measures is demonstrated. We also illustrate how the respective simulated data series as the input variable to the network could contribute to improve the accuracy of volatility forecasting.

关键词： Exchange rate volatility GARCH models Artificial neural networks particle swarm optimization algorithm Simulated data series

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Mission Reliability-Oriented Selective Maintenance optimization for Intelligent Multistate Manufacturing Systems With Uncertain Maintenance Quality

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IEEE ACCESS 2019年 7卷 109804-109816页

作者： Chen, Zhaoxiang He, Yihai Zhao, Yixiao Han, Xiao Liu, Fengdi Zhou, Di Wang, Wenzhuo Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China

Selective maintenance is widely used as a reliability-centered maintenance strategy due to the limited maintenance resources. However, existing selective maintenance studies only consider basic reliability, which cannot systematically describe the operating mechanism of a multistate system, thereby resulting in the inability to obtain an optimal maintenance strategy. Moreover, intelligent manufacturing systems are highly representative of typical multistate industrial systems. In this study, a mission reliability-oriented selective maintenance optimization model for intelligent manufacturing systems that considers the uncertain maintenance effect was proposed. First, a new connotation and modeling method for mission reliability based on multistate system theory was presented to comprehensively characterize the operating mechanism of intelligent manufacturing systems. Second, a quantitative model between maintenance resources and quality based on real-time data was established to reflect the uncertain characteristics caused by repairmen and tools. Third, a selective maintenance decision model of a multistate manufacturing system was developed under the constraints of maintenance cost and time. This constraint combination optimization problem was solved using the particle swarm optimization algorithm. Finally, a case study of selective maintenance optimization for a cylinder head manufacturing system was presented to verify the proposed method.

关键词： Multistate system manufacturing system selective maintenance mission reliability particle swarm optimization algorithm

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Low-level control technology of micro autonomous underwater vehicle based on intelligent computing

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第4期22卷 S8569-S8580页

作者： Zhang, Lanyong Zhang, Lei Liu, Sheng Zhou, Jiajia Papavassiliou, Christos Harbin Engn Univ Coll Automat Harbin 150001 Heilongjiang Peoples R China Imperial Coll London London SW7 2AZ England State Key Lab Millimeter Waves Nanjing 210096 Jiangsu Peoples R China

Based on the modeling of a micro autonomous underwater vehicle, an improved control structure and underlying control method are proposed for some parameters such as the automatic orientation, automatic depth, height, and speed of the micro autonomous underwater vehicle. A cascade double closed-loop control structure is proposed to control the horizontal plane by controlling properties such as the automatic depth, height, positioning, the response speed and adjustment precision of the control are improved. The parameters of the proportional-integral-derivative (PID) control method can be optimized by using particle swarm optimization (PSO), and the fuzzy controller is designed to compare with the PID control of the autonomous underwater vehicles. Compared with the traditional PID control, the control effect of PSO-PID controller is stronger than that of the tranditional PID controller. Due to the uncertainty of the micro autonomous underwater vehicle mathematical model, the position control of PID controller is weaker than the fuzzy controller. The simulation results show that the proposed method has fast dynamic response and acceptable robustness.

关键词： Micro autonomous underwater vehicle particle swarm optimization algorithm Fuzzy control Intelligent computing Low-level control technology

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