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检索条件"主题词=particle Swarm Optimization algorithm"

共 1417 条记录，以下是141-150 订阅

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Improved particle swarm optimization algorithm for optimum charge plan for steelmaking-continuous casting production scheduling

Improved particle swarm optimization algorithm for optimum c...

引用

IEEE International Conference on Industrial Technology (ICIT)

作者： Jian, W Xuc, YC Huang, J Hubei Automot Ind Inst Dept Elect Engn Shiyan Peoples R China

ISBN: (纸本)0780386620

An optimum furnace charge plan model for steelmaking continuous casting planning and scheduling is presented. An improved particle swarm optimization is presented to solve the optimum charge plan problem. Simulations have been carried and the results show that the improved PSO has good performance than the standard PSO. This improved PSO has been used to solve the optimum charge plan problem. The computation with practical data shows that the model and the solving method are very effective.

关键词： particle swarm optimization algorithm charge plan steelmaking process modeling simulation

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A Hybrid Whale optimization and particle swarm optimization algorithm 8

A Hybrid Whale Optimization and Particle Swarm Optimization ...

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IEEE International Conference on Progress in Informatics and Computing (IEEE PIC)

作者： Yuan, Zijing Li, Jiayi Yang, Haichuan Zhang, Baohang Univ Toyama Fac Engn Toyama 9308555 Japan

ISBN: (纸本)9781665426558

In the field of optimization algorithms, hybrid algorithms are increasingly valued by researchers for their effectiveness in improving algorithmic *** recent years, a new type of natural meta-heuristic algorithm called whale optimization algorithm has been proposed. The algorithm refers to whales in nature and imitates their three different feeding methods to solve realistic optimization problems. The particle swarm algorithm, on the other hand, is an algorithm proposed by imitating the way a flock of birds transmits information. As population intelligence algorithms, the accuracy of these two algorithms are not high enough in the convergence process. At the same time, they tend to fall into the local optimum. In this paper, a hybrid algorithm based on whale optimization algorithm and particle swarm algorithm is proposed to update the population by a kind of selection iteration. The experimental results confirm that the algorithm has excellent superiority in convergence accuracy and convergence speed.

关键词： whale optimization algorithm meta-heuristic algorithm particle swarm optimization algorithm exploration and exploitation

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Research on classification of privacy protection based on Improved particle swarm optimization algorithm

Research on classification of privacy protection based on Im...

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4th International Conference on Frontiers of Manufacturing and Design Science (ICFMD 2013)

作者： Chen Yu Tang Yuanxin Zhou Zhou Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China

ISBN: (纸本)9783037859926

In this paper, through the analysis of the characteristics of particle swarm optimization algorithm, combined with the specific circumstances of Bayesian network structure learning, proposed to based on improved particle swarm algorithm. The algorithm uses the BIC measure function as a standard Bayesian network, while preserving the optimal particle case, the possibility of a mutation operation is added to decrease the algorithm into a local optimum. Through a typical Asia network, show that the algorithm is feasible, and other related algorithm is better than the experiment, the effectiveness of the algorithm. In this paper, the algorithm is verified from two aspects of theory and experiments, the results show that the algorithm is feasible.

关键词： Privacy protection Bayesian networks data mining particle swarm optimization algorithm

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A Improved particle swarm optimization algorithm for the Application with Boundary-layer Problems of Power-law Fluids

A Improved Particle Swarm Optimization Algorithm for the App...

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International Conference on Mechanical Engineering and Instrumentation (ICMEI)

作者： Zhou, Jianhui Yue, Lixi Lu, Yannan Hebei United Univ Coll Qianan Tangshan 064400 Peoples R China

ISBN: (纸本)9783038350095

In fluid mechanics, how to solve power-law fluids in ordinary differential equations is always a concerned and difficult problem. we use generally a shooting method to tackle the boundary-layer problems under a suction/injection as well as a reverse flow boundary conditions. A improved particle swarm optimization algorithm (ISPO) is proposed for solving the parameter estimation problems of the multiple solutions in fluid mechanics. This algorithm has improved greatly in precision and the success rate. In this paper, multiple solutions can be found through changing accuracy and search coverage and multi-iterations of computer. Parameter estimation problems of the multiple solutions of ordinary differential equations are calculated, and the result has great accuracy and this method is practical.

关键词： particle swarm optimization algorithm Ordinary differential equations Parameter estimation boundary-layer problems

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Optimal DFIG-Based Wind Turbine Speed And Reactive Power Control Using particle swarm optimization algorithm 2

Optimal DFIG-Based Wind Turbine Speed And Reactive Power Con...

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2nd International Conference on Electrical Engineering and Automatic Control (ICEEAC)

作者： Harkat, Djoumana Bouchemha, Amel Yousfi, Laatra Kahla, Sami Echahid Cheikh Larbi Tebessi Univ Tebessa LAMIS Lab Tebessa Algeria Echahid Cheikh Larbi Tebessi Univ Tebessa LAVIA Lab Tebessa Algeria Res Ctr Ind Technol CRTI Algiers LAVIA Lab Tebessa Algeria

ISBN: (纸本)9798350349740;9798350349757

As the demand for alternative energy sources grows, wind energy systems are developing at a rapid pace. In this research paper, optimally tuned PI controllers are used for controlling a wind turbine based on of a Doubly Fed Induction Generator (DFIG). The particle swarm optimization (PSO) algorithm for PI controller parameters tuning, with the objective of ensuring better energy quality and system performance. The speed Integral Square Error is used as a fitness function of the PSO. The performance evaluation of controllers with PSO implementation is carried out by the simulation through MATLAB /Simulink under linear and nonlinear load conditions. The PI and PI-PSO implementation results comparison proves the superiority of the PI-PSO controller.

关键词： Wind turbine Doubly-fed induction generator particle swarm optimization algorithm PI controller

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Study on the application of particle swarm optimization algorithm to power regulation of CPS in interconnect power grids

Study on the application of particle swarm optimization algo...

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IEEE Electrical Power and Energy Conference

作者： Liu Bin Wang Keying Zou Qing South China Univ Technol Coll Elect Power Guangzhou 510641 Peoples R China Guangdong Med Coll Sch Pub Hlth Dept Biomed Engn Guangzhou 523808 Peoples R China

ISBN: (纸本)9781424428946

The proportion of different AGC unit under the shortfall of power in the grid is investigated. The paper adopt PSO algorithm to optimize units of AGC (automatic generation control) which regulate the distribution of power and be in CPS controlling strategy. The optimal solution is hard to obtain by using average distribution but can be worked by using standard PSO algorithm, and then the CPS level can achieve the optimal under economic conditions. The paper proved the effectiveness of the algorithm by computing example simulation.

关键词： particle swarm optimization algorithm control performance standard automatic generation control regulation power

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Research on Prediction Intervals of Greenhouse Tomato Yield Based on Improved particle swarm optimization algorithm 36

Research on Prediction Intervals of Greenhouse Tomato Yield ...

引用

36th Chinese Control and Decision Conference (CCDC)

作者： Yu, Xiaoyu Luo, Yuzhu Wang, Jianjun Tian, Shujiao Peng, Xiuyuan Liaoning Acad Agr Sci Inst Informat Shenyang Peoples R China

ISBN: (纸本)9798350387780;9798350387797

Tomato is the main cultivated crop in China, and greenhouse tomato can improve the quality and yield of tomato by improving the planting environment. Predicting greenhouse tomato yield is an important basis for making production plan, and the accuracy of yield prediction directly affects economic benefits. In order to improve the accuracy of the prediction intervals model of greenhouse tomato yield, a new objective function was proposed to improve the Lower Upper Bound Estimation (LUBE) prediction intervals model. Then, based on the improved LUBE prediction intervals model, the particle swarm optimization algorithm (PSO) was further improved by adaptive inertia weight and escape strategy. Finally, a LUBE prediction intervals model based on the improved PSO algorithm was proposed in this paper. The prediction intervals of greenhouse tomato yield was simulated by using LUBE prediction intervals model, the improved LUBE prediction intervals model and the LUBE prediction intervals model based on the improved PSO algorithm. The experimental results showed that the prediction intervals coverage probability (PICP) of the LUBE prediction intervals model, the improved LUBE prediction intervals model and the LUBE prediction intervals model based on the improved PSO algorithm were 75%, 80% and 80%, respectively, and the prediction intervals normalized average width (PINAW) were 0.3938, 0.3725 and 0.3629, respectively. It can be concluded that the LUBE prediction intervals model based on the improved PSO algorithm has higher prediction accuracy and better fitting ability.

关键词： adaptive inertia weight escape strategy particle swarm optimization algorithm greenhouse tomato prediction intervals

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An Improved particle swarm optimization algorithm for Parameters Optimizing of Feedforward Neural Networks 14

An Improved Particle Swarm Optimization Algorithm for Parame...

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14th International Conference on Advanced Computational Intelligence

作者： Zhang, Xiaoping Yang, Tianhang Wang, Li He, Zhonghe Liu, Shida North China Univ Technol Sch Elect & Control Engn Beijing Peoples R China

ISBN: (数字)9781665470452

ISBN: (纸本)9781665470469;9781665470452

Deep learning is an important branch of neural networks, which has high accuracy in classification and regression problems, and has been widely used. However, its performance is greatly affected by the parameters. In this paper, an improved particle swarm algorithm named as PSO-C is proposed to automatically train the parameters of the feedforward neural networks. In the proposed algorithm, the curiosity factor is introduced to divide the particles into two categories with different curiosity characteristics so as to improve the exploration ability and information mining ability of the particle swarms. At the same time, a chaotic factor is also introduced to avoid the local optimum problem during the neural network's training. The simulation results show that the PSO-C has better optimization effect on the whole.

关键词： particle swarm optimization algorithm curiosity deep learning artificial neural network parameter optimization

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A particle swarm optimization algorithm with Time Varying Parameters 30

A Particle Swarm Optimization Algorithm with Time Varying Pa...

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30th Chinese Control and Decision Conference (CCDC)

作者： Hu, Zhen Zou, Dexuan Kong, Zhi Shen, Xin Jiangsu Normal Univ Sch Elect Engn & Automat Xuzhou 221000 Jiangsu Peoples R China Northeastern Univ Qinhuangdao Sch Elect Engn & Automat Qinhuangdao 066004 Hebei Peoples R China

ISBN: (纸本)9781538612446

A particle swarm optimization (TVPSO) algorithm with time varying parameters is proposed to improve the performance of particle swarm optimization (PSO) algorithm by two improvements. Aiming at the fact general PSO algorithms have the disadvantages of falling into local optima caused by linearly decreased inertia weight. TVPSO uses the related properties of the trigonometric function to improve the dynamic changes of inertia weight along With Time. The inertia weight maintains a large value in the initial stage, and decreases gradually and reaches a small value at the end. Thus, the global search capability and convergence performance were improved;In order to cope with changes in inertia weight, learning factors also change with time. TVPSO and the other latest particle swarm optimization algorithms are tested on 10 functions at the same time. Experimental results show that TPSO has faster search speed and stronger global search capabilities.

关键词： Time varying parameters particle swarm optimization algorithm Trigonometric function

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particle swarm optimization algorithm Improvement and Application

Particle Swarm Optimization Algorithm Improvement and Applic...

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2010 3rd IEEE International Conference on Computer Science and Information Technology—ICCSIT 2010

作者： Xiaoli Baojunjie and Kuanghang Department of Computer Science,Chongqing Education College Chongqing,400067,China

in this paper,through the research of the existing particle swarm optimization algorithm and its improved algorithm,a particle swarm optimization algorithm improvement program is proposed,and the experimental results show that this improved algorithm not only does not increase the complexity,but also has greater improvement in the convergence speed and stability comparing with the original algorithm.

关键词： particle swarm optimization algorithm dynamic weight aggregation population diversity convergence speed

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