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2010 49th IEEE Conference on Decision and Control

作者： Chunqiu Wan Jun Wang Geng Yang Xing Zhang Department of Automation Tsinghua University Beijing 100084 P. R. China Department of Control Science and Engineering Tongji University Shanghai 201804 P. R. China School of Aerospace Tsinghua University Beijing 100084 P. R. China

ISBN: (纸本)9781424477456

In this paper, a particle swarm optimization algorithm with Gaussian mutations, denoted by GPSO, is proposed to solve constrained optimization problems. Two Gaussian mutation operators are employed to search the promising regions for better solutions. One operator is for the region between the personal best position and the global best one. The other operator is for the region around the global best position. The Gaussian mutations help the population jump out of local optima and find better solutions with more probability. The feasibility-based method compares the performance of different particles. Evaluated by three typical optimization problems, GPSO is more accurate, robust and efficient for locating global optima. The GPSO method is applied to a wind-farm micrositing problem. Simulation results demonstrate that the power generation of the wind farm is further improved while the execution time is substantially reduced.

关键词： Wind farms particle swarm optimization Gauss execution time Power production micro Wind Mutation particle swarm algorithm

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Annual Runoff Forecast Based on Cooperative particle swarm Projection Pursuit Regression Model

Annual Runoff Forecast Based on Cooperative Particle Swarm P...

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Xinxin Li Jing Xu College of Computer and Information Engineering Xinjiang Agricultural University Urumqi China

ISBN: (数字)9781728158570

ISBN: (纸本)9781728158587

According to the high-dimensional nonlinear problem of annual runoff prediction, to build runoff forecasting model based on projection pursuit regression model of Hermite polynomials and the cooperative particle swarm optimization algorithm. Projection pursuit prediction model projects high-dimensional data into low-dimensional space based on sample data driving, completely according to the sample data driven to enhance the prediction results objectivity. The particle swarm optimization algorithm combines the idea of co-evolution to optimize the projection direction and polynomial coefficients in parallel, and further improve the convergence rate and prediction accuracy of the model. The model is applied to the flow prediction of Jiubujiang River Reservoir. The relative error of runoff prediction is less than 15%, and the prediction result is high precision and reliability. The experimental results show that it is feasible and effective to use the cooperative particle swarm projection pursuit regression model to predict the annual runoff.

关键词： projection pursuit regression model co-evolution algorithm particle swarm algorithm Hermite polynomial annual runoff prediction Hermite polynomials particle swarm algorithm Annual runoff Projection Pursuit Regression particle swarm optimization evolutionary algorithm Data sampling data driven

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A hybrid discrete particle swarm optimization for Job shop Scheduling

A hybrid discrete particle swarm optimization for Job shop S...

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International Conference on Computational Aspects of Social Networks(国际社会网络计算会议 CASoN 2010)

作者： Wanliang Wang Jing Zhang Haiyan Wang Xinli Xu Jing Jie College of Computer Science and Technology Zhejiang University of Technology JUT Hangzhou P.R.Chin College of Information Engineering Zhejiang University of Technology ZJUT Hangzhou P.R.China College of Computer Science and Technology Zhejiang University of Technology JUT Hangzhou P.R.Chin

The job shop scheduling problem (JSSP) is a well known NP-hard problem, and many algorithms have been presented to solve it, but the results are still unsatisfactory. In this paper, a hybrid discrete particle swarm optimization algorithm based on a two layer population structure is proposed to solve the JSSP, meanwhile add an improved simulated annealing algorithm to increase the ability of finding the global optimum solutions. The experimental results illustrate the high effectiveness of the proposed method, which can avoid prematurity efficiently and be more robust than the PSO and DPSO.

关键词： component particle swarm algorithm job shop scheduling makespan simulated annealing algorithm

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Network Repair Strategy Based on the Failure of Airline

Network Repair Strategy Based on the Failure of Airline

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2016 4th International Conference on Electrical&Electronics Engineering and Computer Science(ICEEECS 2016)

作者： Zhang Yuxiang Wu Minggong Wang Xiaorong Wen Xiangxi College of Air Traffic Control and Navigation Air Force Engineering University

ISBN: (纸本)9781510835429

Contraposing to the network repair problem of airline network after edge failure, an optimization model was proposed based on network repair study and hub airline network theory. The model regarded the best performance under certain cost as the objective function. And it satisfied different requirements by giving different weights. The model considerated the cost caused by distance and congestion. The network efficiency was selected as measurement for performance. The model was solved by improved particle swarm optimization. At last, taking the airline between Hang zhou and Xian yang as an example, a simulation was conducted to verify the model. The experimental result shows that capacity decision coefficient can control the cost well caused by congestion through changing the node capacity, which can affect the allocation method of traffic and has an influential effect on network survivability. What's more, the model can well adjust the balance between cost and network performance. The model has a good application prospect.

关键词： Airline network Network repair Capacitated constraint Network optimization particle swarm algorithm

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Calculating method of optimal path of data of traffic network

Calculating method of optimal path of data of traffic networ...

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2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics(AMEII 2015)

作者： Sun- Hongfeng Li-Ying Yang-Yanchun Information Technology School Shandong Women's University

ISBN: (纸本)9781510806481

According to the influence of traffic flow change on the selection of vehicle routing, to find a solution to the planning of travel routes in the situation that knowing the change of traffic flow. By using the method of particle swarm algorithm combined with dynamic programming to optimize the path of the vehicle, we can get the excellent route of each vehicle under the influence of the traffic flow. Simulating based on the road network structure and traffic data in real environment, the results showing that this method can increase the authenticity and dynamic of path optimization, and the road traffic flow and vehicle travel time having important influence on vehicle routing choice.

关键词： Traffic flow particle swarm algorithm Dynamic programming The optimization of path Time dependent

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particle swarm Optimization as an Efficient Computational Method in order to Minimize Vibrations of Multimesh Gears Transmission

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ADVANCES IN ACOUSTICS AND VIBRATION 2011年第unknown期2011卷 195642-1-195642-6页

作者： Carbonelli, Alexandre Perret-Liaudet, Joel Rigaud, Emmanuel Le Bot, Alain Univ Lyon Ecole Cent Lyon Lab Tribol & Dynam Syst UMR CNRS 5513 36 Ave Guy Collongue F-69134 Ecully France

The aim of this work is to present the great performance of the numerical algorithm of particle swarm Optimization applied to find the best teeth modifications for multimesh helical gears, which are crucial for the static transmission error (STE). Indeed, STE fluctuation is the main source of vibrations and noise radiated by the geared transmission system. The microgeometrical parameters studied for each toothed wheel are the crowning, tip reliefs and start diameters for these reliefs. Minimization of added up STE amplitudes on the idler gear of a three-gear cascade is then performed using the particle swarm Optimization. Finally, robustness of the solutions towards manufacturing errors and applied torque is analyzed by the particle swarm algorithm to access to the deterioration capacity of the tested solution.

关键词： computational methods Transmission systems order Static transmission error particle swarm optimization manufacturing error Gears Helical gears particle swarm algorithm

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Application of hybrid neural network in discharge coefficient prediction of triangular labyrinth weir

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FLOW MEASUREMENT AND INSTRUMENTATION 2022年 83卷

作者： Wang, Fei Zheng, Shengyi Ren, Yiming Liu, Weidong Wu, Chao Hohai Univ Coll Energy & Elect Engn 8 Fochengxi Rd Nanjing 211100 Peoples R China Hohai Univ Coll Publ Adm 8 Fochengxi Rd Nanjing 211100 Peoples R China

Discharge coefficient (Cd) is an important parameter of triangular labyrinth weir. It is of great significance to predict the discharge coefficient accurately. In this research, in order to more accurately predict the Cd, in view of the traditional BP neural network is easy to fall into the local minimum in the training process, genetic algorithm (GA) and particle swarm optimization (PSO) are employed to optimize the traditional BP neural network's initial weights and thresholds. Nonlinear regression analysis (NLR) is also added to compare with these intelligent methods and four discharge coefficient prediction models are built, namely the NLR, the BPNN, the GA-BPNN and the PSO-BPNN. After the completion of the model construction, in order to objectively evaluate the performance of these models, the prediction results of these models are compared with the experiment results, and the determination coefficient (R2), the mean absolute error (MAE) and the root mean square error (RMSE) are introduced as the performance indicators to quantify the model performance. The results show that the accuracy and stability of the NLR are much worse than that of the intelligent models. The prediction results of the GABPNN and the PSO-BPNN are quite accurate with a higher decision coefficient than the BPNN. Moreover, the MAEs and the RMSEs of the GA-BPNN and the PSO-BPNN were significantly reduced by 25 and 40% compared with BPNN, respectively, and the maximum prediction errors were 4.4% and 2.6%, severally. Meanwhile, the width of error uncertainty band of GA-BPNN and PSO-BPNN is narrower than BPNN. By comparing GA-BPNN and PSO-BPNN with the discharge coefficient prediction models of triangular labyrinth weir in previous literatures, it is found that the mean absolute percentage error (MAPE) values of GA-BPNN and PSO-BPNN are 1.504% and 1.225% respectively, which are lower than other existing models. At the same time, the other performance indexes are better than most existing model

关键词： Discharge coefficient Genetic algorithm particle swarm algorithm Triangular labyrinth weir Neural network

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Multi-objective isogeometric integrated optimization for shape control of piezoelectric functionally graded plates

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2021年 377卷 113698-113698页

作者： Wang, Chao Yu, Tiantang Shao, Guojian Tinh Quoc Bui Hohai Univ Dept Engn Mech Nanjing 211100 Peoples R China Anhui Polytech Univ Dept Architectural Engn Wuhu 241000 Peoples R China Jiangsu Prov Wind Power Struct Engn Res Ctr Nanjing 211100 Peoples R China Duy Tan Univ Inst Res & Dev Da Nang City Vietnam Tokyo Inst Technol Dept Civil & Environm Engn Meguro Ku 2-12-1-W8-22 Tokyo 1528552 Japan

This paper presents a novel multi-objective integrated optimization method for static shape control of piezoelectric functionally graded plates (FGPs). The new method combines isogeometric analysis (IGA) and an effective multi-objective non-gradient algorithm which has not been applied to the integrated design of piezoelectric FGPs. Mechanical behavior of the FGPs with surface bonded piezoelectric layers is derived using the IGA associated with a third-order shear deformation theory (TSDT). The high-order continuity of NURBS basis functions in IGA meets the demand of C-1-continuity of the TSDT. In optimal design problem, material layout of the FGPs and control voltages of piezoelectric layers are simultaneously optimized under the conditions of minimum static shape error and maximum first-order natural frequency. The B-spline control points for describing ceramic volume fraction distribution of the FGPs and the applied voltages are taken as design variables. In addition, an improved multi-objective particle swarm optimization algorithm is used as an optimization solver. The validity and applicability of this combination of innovative method are demonstrated through several numerical examples in integrated design. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Piezoelectric functionally graded plates Shape control particle swarm algorithm Control voltages Isogeometric analysis Multi-objective optimization

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Triaxial Accelerometer Error Coefficients Identification with a Novel Artificial Fish swarm algorithm

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JOURNAL OF SENSORS 2015年第Pt.2期2015卷

作者： Gao, Yanbin Guan, Lianwu Wang, Tingjun Harbin Engn Univ Coll Automat Inst Inertial Nav & Measurement & Control Technol Harbin 150001 Heilongjiang Peoples R China Queens Univ Nav & Instrumentat Res Grp NavINST Dept Elect & Comp Engn Kingston ON K7L 3N6 Canada China Aerosp Sci & Technol Corp Xian 710001 Shanxi Peoples R China

Artificial fish swarm algorithm (AFSA) is one of the state-of-the-art swarm intelligence techniques, which is widely utilized for optimization purposes. Triaxial accelerometer error coefficients are relatively unstable with the environmental disturbances and aging of the instrument. Therefore, identifying triaxial accelerometer error coefficients accurately and being with lower costs are of great importance to improve the overall performance of triaxial accelerometer-based strapdown inertial navigation system (SINS). In this study, a novel artificial fish swarm algorithm (NAFSA) that eliminated the demerits (lack of using artificial fishes' previous experiences, lack of existing balance between exploration and exploitation, and high computational cost) of AFSA is introduced at first. In NAFSA, functional behaviors and overall procedure of AFSA have been improved with some parameters variations. Second, a hybrid accelerometer error coefficients identification algorithm has been proposed based on NAFSA and Monte Carlo simulation (MCS) approaches. This combination leads to maximum utilization of the involved approaches for triaxial accelerometer error coefficients identification. Furthermore, the NAFSA-identified coefficients are testified with 24-position verification experiment and triaxial accelerometer-based SINS navigation experiment. The priorities of MCS-NAFSA are compared with that of conventional calibration method and optimal AFSA. Finally, both experiments results demonstrate high efficiency of MCS-NAFSA on triaxial accelerometer error coefficients identification.

关键词： CALIBRATION METHODS accelerometers dike swarms strapdown inertial navigation system particle swarm algorithm

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Optimization and Its Implementation Impact of Two-Modes Controller Fractional Approximation for Buck Converters

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MICROMACHINES 2022年第10期13卷 1600-1600页

作者： Sanchez, Allan G. S. Perez-Pinal, Francisco-Javier Espinosa-Calderon, Alejandro CONACYT Tecnol Nacl Mexico Celaya Celaya 38010 Mexico Tecnol Nacl Mexico Celaya Celaya 38010 Mexico Tecnol Nacl Mexico Reg Ctr Optimizat & Device Dev CRODE Celaya 38020 Mexico

Additional degrees of freedom in a fractional-order control strategy for power electronic converters are well received despite the lack of reliable tuning methods. Despite artificial/swarm intelligence techniques have been used to adjust controller parameters to improve more than one characteristic/property at the same time, smart tuning not always leads to realizable structures or reachable parameter values. Thus, adjustment boundaries to ensure controller viability are needed. In this manuscript the fractional-order approach is described in terms of El-Khazali biquadratic module, which produces the lowest order approximation, instead of using a definition. A two-modes controller structure is synthesize depending on uncontrolled plant needs and parameters are adjusted through particle swarm and genetic optimization algorithms for comparison. Two error-based minimization criteria are used to consider output performance into the process. Two restrictions complement the optimization scheme, one seeks to ensure desired robustness while the other prevents from synthesizing a high-gain controller. Optimization results showed similarity between minima obtained and significant difference between parameters of those controller optimized without the proposed constraints was determined. Numerical and experimental results are provide to validate proposed approach effectiveness. Effective regulation, good tracking characteristic and robustness in the presence of load variations are the main results.

关键词： fractional control DC-DC converters optimization algorithms particle swarm algorithm fractional-order implementation

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