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16th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES)

作者： Liu Gui-ying Qu Li-ping Li Yun-feng Beihua Univ Elect Informat Engn Coll Jilin 132021 Jilin Peoples R China JiLin 524 Factory Jilin 132021 Jilin Peoples R China

ISBN: (纸本)9781538621622

In terms of the characteristics of time lag system, the method of multiple models adaptive predictive control based on particle swarm optimization (pso) algorithm is proposed. Multiple Models can approach the dynamic character of the controlled object. We can design corresponding controller to each model. We can get final controlled variable with the limited controlled variable by means of weighting. Each controller adopts Predictive Control method. At last the method gets the global optimum by pso algorithm. We compare the method to PID in time lag system. Simulation result indicates the method not only can overcome inaccuracy of modeling and time variation of parameters but also has good control performance and stronger robustness.

关键词： pso algorithm Multiple models Predictive control Time lag

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A pso algorithm for multi-objective hull assembly line balancing using the stratified optimization strategy

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COMPUTERS & INDUSTRIAL ENGINEERING 2016年第0期98卷 53-62页

作者： Yuguang, Zhong Bo, Ai Yong, Zhan Harbin Engn Univ Coll Mech & Elect Engn Harbin 150001 Peoples R China

Hull assembly line (HAL) is a mixed-model assembly line on which different hull blocks can be assembled at the same time. Aiming at the balance problem of HAL, minimizing the cycle time, minimizing the static load *** workstations, minimizing the dynamic load balancing in all workstations, and minimizing the multi-station associated complexity are considered as optimization objectives. An improved discrete particle swarm optimization (IDpso) algorithm based on the stratified optimization idea is developed for scheduling of the multi-objective problem. In the proposed algorithm, particles were coded by a two-dimensional task-oriented representation method, and then collaboration and competition of particle individuals are simulated by crossover and mutation operators in the genetic algorithm (GA). The performance of the proposed hybrid algorithm is examined over several test problems in terms of solution quality and running time. Finally, a practical case is used to analyze the effectiveness and feasibility of the stratified scheduling strategy. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Assembly line balancing problem Shipbuilding industry pso algorithm Stratified optimization

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Enhancement of heat transfer of confined enclosures with free convection using blocks with pso algorithm

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APPLIED THERMAL ENGINEERING 2016年 101卷 79-91页

作者： Azimifar, A. Payan, S. Univ Sistan & Baluchestan Dept Mech Engn Zahedan 98135161 Iran

In this paper, optimal shape of cavity with change of hot walls geometry is obtained using pso algorithm. In this method near domain of hot wall is divided into similar blocks as those can either be solid or fluid. Free convection is dominant in the cavity. Continuity, momentum and energy equations are discretized by finite volume method. These equations are solved by SIMPLER algorithm. pso algorithm is used for obtaining diffusion parameters near the hot walls. Diffusion coefficients obtained from pso algorithm (non-dimensional parameters for diffusion terms in momentum and energy equations) are replaced in governing equations such a way that average Nusselt number from cold wall to be maximized. The results are obtained for range of Rayleigh numbers and different boundary conditions. Results show that psoA is able to obtain size and location of changes in hot walls. These changes can increase heat transfer from cold wall between 13% and 40% for different boundary conditions. If hot and cold walls are near together, millimeter changes are enough for acceptable percent value of increase of heat transfer, but for cases that hot wall is far from cold wall, we must attach medium or large blocks on hot wall or construct new cavities in accordance with optimized shape obtained from pso algorithm. As told, this method can be applied toconstructed cavities with attachment of rectangular fins. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Free convection Optimized shape pso algorithm

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Research on Independent College Enrollment Expansion and Teaching Quality Assessment Model Based on pso algorithm

Research on Independent College Enrollment Expansion and Tea...

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2017 5th International Education,Economics,Social Science,Arts,Sports and Management Engineering Conference(IEESASM 2017)

作者： Xiuming Yu Yunfei Yu College of Optical and Electronical Information Changchun University of Science and Technology

Teaching evaluation plays an important role in college teaching activity, and how to ensure high teaching quality while the teaching scale is expanding gradually is a core issue in independent colleges. This paper presents an intelligent optimization model based on pso algorithm for the scale of enrollment expansion and teaching evaluation in independent college, to help conduct teaching quality evaluation in the case of limited resources.

关键词： Enrollment expansion pso algorithm Teaching assessment

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An Improved Interval pso algorithm with Dynamic Shrinking 28

An Improved Interval PSO Algorithm with Dynamic Shrinking

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28th Chinese Control and Decision Conference

作者： Guan, Shou-ping Zhang, You-dong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9781467397148

Considering the slow convergence problem of conventional interval particle swarm optimization algorithm based on static shrinking strategy (SIpso), this paper proposes a new dynamic shrinking strategy to form a new interval particle swam optimization algorithm (DIpso) to improve the SIpso, which can make the interval shrinking more flexible and be conducive to the quick convergence. The simulation results show that the efficiency of DIpso is superior to SIpso, and demonstrate the improvement for SIpso is effective

关键词： Interval optimization pso algorithm Dynamic shrinking

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Optimal placement of active braces by using pso algorithm in near- and far-field earthquakes

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International Journal of Advanced Structural Engineering 2016年第1期8卷 29-44页

作者： Mastali, M. Kheyroddin, A. Samali, B. Vahdani, R. Department of Civil Engineering ISISE Minho University Campus de Azurem Guimaraes 4800-058 Portugal Faculty of Civil Engineering Semnan University Seman Iran Department of Civil Engineering and Applied Mechanics University of Texas Arlington TX United States School of Civil and Environmental Engineering University of Western Sydney Sydney Australia

One of the most important issues in tall buildings is lateral resistance of the load-bearing systems against applied loads such as earthquake, wind and blast. Dual systems comprising core wall systems (single or multi-cell core) and moment-resisting frames are used as resistance systems in tall buildings. In addition to adequate stiffness provided by the dual system, most tall buildings may have to rely on various control systems to reduce the level of unwanted motions stemming from severe dynamic loads. One of the main challenges to effectively control the motion of a structure is limitation in distributing the required control along the structure height optimally. In this paper, concrete shear walls are used as secondary resistance system at three different heights as well as actuators installed in the braces. The optimal actuator positions are found by using optimized pso algorithm as well as arbitrarily. The control performance of buildings that are equipped and controlled using the pso algorithm method placement is assessed and compared with arbitrary placement of controllers using both near- and far-field ground motions of Kobe and Chi–Chi earthquakes. © 2016, The Author(s).

关键词： Near- and far-field earthquakes Optimal actuator position Pole assignment method pso algorithm

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Optimal Voltage Unbalance Compensation in a Microgrid Using pso algorithm 7

Optimal Voltage Unbalance Compensation in a Microgrid Using ...

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7th IEEE Power India International Conference (PIICON)

作者： Eilaghi, Sina Fazlollahzadeh Ahmadian, Ali Golkar, Masoud Aliakbar KN Toosi Univ Technol Fac Elect Engn Tehran Iran

ISBN: (纸本)9781467389624

distributed generations (DGs) make the microgrid active and more flexible. In addition of power demand supplying, the DGs can be used for improving power quality indices such as reducing voltage unbalance. However, for purposes of power quality improvement the microgrid controllers' coefficients should be adjusted optimally. Due to microgrid components accurate modeling, the controllers' adjustment is a complex problem that should be solved using an appropriate approach. The purpose of this paper is proposing particle swarm optimization (pso) algorithm so as to find the optimal coefficients of a PI controller for enhancing voltage unbalance factor (VUF) in a microgrid. The objective function is considered minimization of integral of absolute error index which is solved under technical constraints. The results of simulation indicate the robustness and success of the proposed algorythm.

关键词： voltage unbalance compensation microgrid pso algorithm distributed generations

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Comparison of RSM, ANN and pso algorithms to Predict the Porosity Level of Twin Wire Arc Sprayed Aluminium Coatings on AZ31B Magnesium Alloy

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International Journal of Vehicle Structures and Systems 2024年第3期16卷 390-395页

作者： Kumar, L. Govind Sanjeev Thirumalaikumarasamy, D. Karthikeyan, K. Mathanbabu, M. Elango, E. Penmetsa, Ravi Varma Dept. of Mfg. Engg Annamalai Uni Tamil Nadu Chidambaram India Dept. of Mech. Engg. Gujarat Inst. Desert Ecology Gujarat Bhuj India Dept. of Mech. Engg. Govt. College of Engg Tamil Nadu Bargur India Dept. of Mech. Engg Annamalai Uni Tamil Nadu Chidambaram India Dept. of Mech. Engg Sagi Rama Krishnam Raju Engg. College Andhra Pradesh Bhimavaram India

The twin wire arc spray method (TWAS) is one among the efficient methods that is widely recognised and utilised for protecting components in surface coating technology due to its economic viability and adaptability. The quality of coating obtained in TWAS is excellent and the porosity is very low in comparison to other conventional methods. The TWAS spray parameters were optimized by single response optimization to obtain the minimum porosity utilizing statistical tools such as DOE and response surface methodology (RSM). Investigations focused on the influence of power, gas pressure and standoff distance. The analysis was done by three factors and five stages of analysis in the central composite design (CCD). The lowest porosity (1.21%) was obtained at optimum conditions of 5 kW power, 3.5 bar air pressure and 200mm stand-off distance, respectively, for TWAS coatings. The RSM, ANN and pso is utilized to optimise the constructed model. The optimized value obtained through pso and ANN were compared with the RSM. ANN gives a superior outcome when comparing pso and RSM. It is validated by the response and contour plots in order to assist in selecting the spray parameters. The obtained results indicate that the coating porosity depends mostly on the arc current. © 2024. Carbon Magics Ltd.

关键词： Aluminium feedstock AZ31B Magnesium alloy pso algorithm Response surface method Twin wire arc spray

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Prediction of Transformer Oil Temperature Based on an Improved pso Neural Network algorithm

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2024年第1期17卷 29-37页

作者： Zhang, Zhiyan Kong, Weihan Li, Linze Zhao, Hongfei Xin, Chunwen Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou 450002 Peoples R China State Grid Jiangsu Elect Power Co Ltd Maintenance Branch Co Nanjing 211102 Peoples R China State Grid Nanyang Power Supply Co Internet Dept Nanyang 473000 Peoples R China

Introduction In addressing the issue of power transformer oil temperature prediction, traditional back propagation (BP) neural network algorithms have been found to suffer from local optimization and slow convergence. This study proposes an oil temperature prediction model based on an improved particle swarm optimization (pso) neural network algorithm, which introduces an asymmetric adjustment learning factor and a mutation operator. The BP neural network, genetic algorithm (GA) optimization neural network, and the improved pso neural network are compared by considering various factors, such as ambient temperature, load changes, and the number of cooler groups under different working conditions. Results show that the proposed algorithm improves the actual change trend of oil surface temperature and makes the transformer operation more stable to a certain *** The mathematical model for predicting transformer oil temperature is clear, but the parameters in the model are uncertain and vary with time. When subjected to different operating conditions, such as ambient temperature, load changes, and the number of cooler groups acting independently or in combination, the prediction results of the oil temperature model vary with different system *** This paper aims to enhance the accuracy of transformer temperature prediction. In order to optimize the oil temperature prediction model, asymmetric adjustment learning factors and mutant operators are added to meet diverse system parameter *** The paper utilizes an oil temperature prediction model based on an improved pso neural network algorithm, which introduces an asymmetric adjustment learning factor and a mutation operator to address the limitations of the standard pso *** This paper has employed a fusion algorithm of the genetic algorithm of the BP neural network and the pso algorithm, and conducted simulation and experimental analysis. The simulation and experi

关键词： Power transformer oil temperature neural network pso algorithm learning factors genetic algorithm

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Lithium-ion batteries state of charge estimation using pso-BP neural network improved UKF algorithm 39

Lithium-ion batteries state of charge estimation using PSO-B...

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39th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Pu, Long Wang, Chun Chen, Junlin Sichuan Univ Sci & Engn Sch Mech Engn Zigong Peoples R China

ISBN: (纸本)9798350390780;9798350379228

To tackle the issue of inaccurate state of charge (SOC) estimation in lithium-ion batteries when employing the unscented Kalman filter (UKF) in real-world applications, a refined approach utilizing the pso-BP neural network is introduced to enhance the performance of UKF for SOC estimation. Firstly, the Thevenin model is identified using particle swarm optimization (pso). Secondly, the pso-BP is trained by using the characteristic parameters related to the estimation error of UKF. Finally, the UKF SOC estimate error was corrected using the trained pso-BP. Experimental results demonstrate that the proposed algorithm significantly enhances SOC estimation accuracy.

关键词： SOC estimation pso algorithm BP UKF

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