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检索条件"主题词=Particle Swarm algorithm"

共 736 条记录，以下是201-210 订阅

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Position Control of Arm Manipulator Within Fractional Order PID Utilizing particle swarm Optimization algorithm

Position Control of Arm Manipulator Within Fractional Order ...

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IEEE Conference on Power Electronics and Renewable Energy (CPERE)

作者： Tasneem Mohamed Ahmed Amr Nasr A. E. Gaber Ragi Hamdy Ayman S. Abdel-Khalik Faculty of engineering Alexandria University Alexandria Egypt Alexandria Higher Institute of Engineering and Technology Alexandria Egypt

ISBN: (纸本)9781728109114

Rigid robots can be used in several industrial applications including assembly of electronic parts, welding and dangerous jobs that have danger in human life. In this paper the main goal is to control the position of a 2-DOF robot arm. Proportional Integration-Derivative (PID) controller can perform this work but its robustness cannot be guaranteed. Another controller with a higher sensitivity and accuracy is required. Fractional Order PID (FOPID) controller is adopted for such a task. The controller parameters are optimized using particle swarm Optimization (PSO) approach. The robot arm is used in order to move objects from one point to another so it is proposed a FOPID controller to eliminate the error between the desired trajectory and the actual displacement of the robot end-effector.

关键词： FOPID controller particle swarm optimization 2-DOF robot particle swarm optimization Position control Proportional Integral Derivative Control Robot fractional order Pelvic Inflammatory Disease Arm spiral arm Controllers robotic arm particle swarm algorithm

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Consensus Clustering Based on particle swarm Optimization algorithm

Consensus Clustering Based on Particle Swarm Optimization Al...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Ahmed. A. A. Esmin Rodrigo A. Coelho Department of Computer Science Federal University of Lavras - UFLA Lavras MG Brazil Department of Computer Science Federal University of Lavras - UFLA Lavras MG Brazil

ISBN: (纸本)9781479906505

Clustering is an important data mining task and has been explored extensively by a number of researchers for different application areas. The clustering ensembles combine multiple partitions generated by different clustering algorithms into a single clustering solution. Clustering ensembles have emerged as a prominent method for improving robustness, stability and accuracy of clustering solutions. So far, many contributions have been made to find consensus clustering. One of the major problems in clustering ensembles is the consensus function. In this paper, the particle swarm Optimization algorithm (PSO) is proposed to solve the consensus clustering problem. We find that the particle swarm clustering algorithm is efficient for this problem. An empirical study compares the accuracy of our proposed algorithms with other consensus clustering methods including voting on five data sets. The experimental results show that the PSO consensus clustering method produces clustering's that are as good as, and often better than, these other methods.

关键词： component particle swarm optimization cluster data consensus and ensemble clustering particle swarm optimization Cluster Analysis Clustering algorithms particle swarm algorithm Ensembles Clustering methods Vote

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Determine the optimal switching angles symmetrical cascaded multilevel inverter using Improved particle swarm optimization algorithm

Determine the optimal switching angles symmetrical cascaded ...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Sajjad Ahmadi Mostafa Andalib Sahnesaraie Saeid Hojjati Talami Fakhteh Dini Mahnaz Mohebbi Zanganeh Yazdan Ashgevari Department of electrical engineering Islamic Azad University Ardabil Iran Alborz Electric Power Distribution Company Alborz Iran Department of electrical engineering Islamic Azad University Mehriz Iran Faculty of electrical & Computer engineering Shahid Beheshti University Tehran Iran

ISBN: (数字)9781728131498

ISBN: (纸本)9781728131504

With the increasing use of inverters in the industry, the need to reduce the output harmonics is felt more than ever. Interstellar multi-level structures have grown exponentially due to benefits such as harmonic reduction and loss reduction. In multilevel inverters, nonlinear and complex equations become more complex when the number of surfaces increases. One of the ways to find optimal keying angles is to reduce the harmonics of the whole Newton-Raphson method. One of the main disadvantages of the Newton-Raphson method is the strong dependence of the answers to the initial guesses. By choosing different initial guesses, different answers may be obtained or results will not converge. And their solution is not possible by conventional methods of numerical solution of nonlinear equations such as the Newton-Ruffson method. In this thesis, one of a variety of Intelligent algorithms is used to solve the Newton-Raphson method problem. The particle swarm algorithm has been selected based on the proper background in solving problems of high complexity to solve the problem of optimal keying angles in this dissertation. In this thesis, the particle swarm algorithm has been improved. Simulation results for various multilevel converters have been presented and represent the high efficiency of the improved type of particle swarm algorithm in determining the appropriate angle of fire for reducing the high harmonics and generating a waveform with very low harmonic distortion and near sinusoidal.

关键词： Multilevel Inverters Selective Harmonic Elimination particle swarm Optimization algorithm Improved particle swarm Optimization algorithm particle swarm algorithm Inventors Newton-Raphson method Theses multilayers harmonics Angle Optimal keying loss reduction

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Observation Points-Based particle swarm Optimization algorithm

Observation Points-Based Particle Swarm Optimization Algorit...

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IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)

作者： Shengsheng Xu Yulin He Joshua Zhexue Huang College of Computer Science & Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728165509

ISBN: (纸本)9781728165516

particle swarm optimization is a heuristic random optimization algorithm, in which each particle pursues its own optimal particle and the global optimal position. In the random search process of particle swarm optimization, these particles will eventually converge to the population optimal particle. The algorithm has the shortcoming of premature convergence because it is a stochastic approximate optimization algorithm. In this paper, the concept of random observation points is defined. Based on the standard PSO algorithm, a particle swarm optimization algorithm based on random observation points (OBPSO) which replaces the particles in the original particle swarm optimization by setting random observation points in each algorithm iteration is proposed. By comparing the experimental results of this algorithm with those of standard PSO and other variants on benchmark functions, OBPSO algorithm has the advantages of avoiding premature convergence and improving the global search ability of standard PSO algorithm, which effectively improves the search performance of standard PSO algorithm.

关键词： Observation points particle swarm optimization global optimum local optimum multimodal function unimodal function particle swarm optimization observation points global optimum local optimum algorithms particle swarm algorithm Search process Optimization algorithms parametric subharmonic oscillator

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An Improved particle swarm Optimization algorithm Combined with Invasive Weed Optimization

An Improved Particle Swarm Optimization Algorithm Combined w...

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Huan Zhao Xin Wang Zhongze Jiao Wen Zeng Jinxiao Dou Jianglong Yu Shenyang Aerospace University Institute of Thermal Energy Shenyang China University of Newcastle Chemical Engineering Callaghan Australia Key Laboratory of Advanced Coal and Coking Technology of Liaoning Province University of Science and Technology Liaoning Anshan China

ISBN: (纸本)9781728152103

This paper presents a hybrid algorithm based on the invasive weed optimization (IWO) and particle swarm optimization (PSO), named IW-PSO. By incorporating the reproduction and spatial dispersal of IWO into the traditional PSO, exploration and exploitation of the PSO can be enhanced and well balanced to achieve better performance. In a set of 15 test function problem, computational results, preceded by analysis and selection of IW-PSO parameters, show that IW-PSO can improve the search performance. In the other comparative experiment with fixed iteration, the IW-PSO algorithm is compared with various more up-to-date improved PSO procedures appearing in the literature. Comparative results demonstrate that IW-PSO can generate quite competitive quality solution in stability, accuracy and efficiency. As evidenced by the overall assessment based on two kinds of computational experience, IW-PSO can effectively obtain higher quality solutions so as to avoid being trapped in local optimum.

关键词： particle swarm optimisation particle swarm optimization Invasive Weeds parametric subharmonic oscillator particle swarm algorithm local optimum algorithms

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particle swarm Optimization algorithm with Asymmetric Time Varying Acceleration Coefficients

Particle Swarm Optimization Algorithm with Asymmetric Time V...

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2009 IEEE International Conference on Robotics and Biomimetics(2009 IEEE 机器人与仿生技术国际会议 ROBIO 2009)

作者： G. Q. Bao K. F. Mao Electrical Engineering Department Gansu University of TechnologyChina

ISBN: (纸本)9781424447749

To update the performance of the standard optimization method, a modified particle swarm optimization (MPSO) algorithm is proposed based on the earlier works. An asymmetric time-varying acceleration coefficients adjusting strategy introduced in this paper is to keep the balance between the global search and the local search with the great advantages of convergence property and robustness compared with basic PSO algorithm. The relationship between swarm average velocity and convergence is studied through Benchmark test functions simulation. All the merits mentioned above are demonstrated by the compound gear transmission ratio optimization in transmission systems.

关键词： speedup factor particle swarm algorithm Benchmark testing time varying Transmission systems particle swarm optimization optimization method Converge

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The Identification Strategy of Bouc-Wen Model Based on Improved particle swarm Optimization algorithm

The Identification Strategy of Bouc-Wen Model Based on Impro...

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第33届中国控制与决策会议

作者： Zicheng Li Ruirui Xu Hou-Neng Wang Tao Xiong School of Electrical and Information Engineering Wuhan Institute of Technology

The piezoelectric micro-positioning system consists of a piezoelectric actuator that operates a positioning *** nonlinearity is one of the significant variables limiting the positioning precision of these *** paper proposes an asymmetrical Bouc-Wen(B-W) model to describe the hysteresis of piezoelectric *** order to solve the problem of low parameter identification accuracy,an improved particle swarm optimization(IPSO) algorithm is *** inertial weight and learning factor nonlinear change strategy is proposed to quickly find the optimal model *** results show that the algorithm has better overall searching ability in the early stage and faster convergence speed compared with the standard particle swarm optimization(PSO).The proposed algorithm can improve the solution accuracy and accelerate the global convergence.

关键词： Bouc-Wen Model particle swarm algorithm Parameter Identification Hysteresis Nonlinearity

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Improved particle swarm optimization algorithm to multi-objective optimization energy hub model with P2G and energy storage

Improved particle swarm optimization algorithm to multi-obje...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Rui Ma Jianbo Deng Hao Li Jiaqian Qin School of Electrical & Information Engineering Changsha University of Science and Technology Changsha China

ISBN: (纸本)9781538614280

In this paper, an Energy hub mathematical model with power-to-gas(P2G), energy storage, CCHP and electric energy feedback is constructed. Considering the factors such as the rate structure and uncertainty of wind power output, the energy hub model takes the minimum fuel cost of energy hub operation and the minimum interactive energy costs with power grid as the optimization objective to optimize. In order to solve the high dimension complex problem, an improved multi-objective particle swarm optimization algorithm is proposed to solve the optimization problem, with the consideration of the bad experience during the speed update, the repeated search to the worst position is avoided., the search efficiency of the algorithm is improved and the proof is carried out; By adopting the global optimal solution selection strategy based on the interval and the optimal selection strategy, it can obtain the approximate Pareto front end which is better than the convergence and diversity of the basic MOPSO algorithm. The feasibility of the model and the effectiveness of the improved algorithm are verified by an example.

关键词： P2G Wind power uncertainty Combined cooling Heating and power Energy hub combined cooling ELAVL2 gene HUB energy storage Energy costs rate structure particle swarm algorithm particle swarm optimization

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A Parallel particle swarm Optimization algorithm for Option Pricing

A Parallel Particle Swarm Optimization Algorithm for Option ...

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IEEE International Symposium on Parallel and Distributed Processing

作者： Hari Prasain Girish Kumar Jha Parimala Thulasiraman Ruppa Thulasiram Department of Computer Science University of Manitoba

ISBN: (纸本)9781424465330

Option pricing is one of the challenging problems of computational finance. Nature-inspired algorithms have gained prominence in real world optimization problems such as in mobile ad hoc networks. The option pricing problem fits very well into this category of problems due to the ad hoc nature of the market. particle swarm optimization (PSO) is one of the novel global search algorithms based on swarm intelligence. We first show that PSO could be effectively used for the option pricing problem. The results are compared with standard classical Black-Scholes-Merton model for simple European options. In this study, we developed two algorithms for option pricing using particle swarm Optimization (PSO). The first algorithm we developed is synchronous option pricing algorithm using PSO (SPSO), and the second is parallel synchronous option pricing algorithm. The pricing results of these algorithms are close when compared with classical Black-Scholes-Merton model for simple European options. We test our Parallel Synchronous PSO algorithm in three architectures: shared memory machine using OpenMP, distributed memory machine using MPI and on a homogeneous multicore architecture running MPI and OpenMP (hybrid model). The results show that the hybrid model handles the load well as we increase the number of particles in simulation while maintaining equivalent accuracy.

关键词： particle swarm optimization Pricing parametric subharmonic oscillator algorithms Hybrid modes ad hoc memory machine particle swarm algorithm message passing mannose phosphate isomerase remote procedure calls SEARCHING algorithm

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CONTROLLER DESIGN FOR ROTARY INVERTED PENDULUM SYSTEM USING particle swarm OPTIMIZATION algorithm

CONTROLLER DESIGN FOR ROTARY INVERTED PENDULUM SYSTEM USING ...

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IEEE Canadian Conference on Electrical and Computer Engineering

作者： Afshin Rahimi Kaamran Raahemifar Krishna Kumar Hekmat Alighanbari Ryerson University Toronto Ontario Canada

ISBN: (纸本)9781479900312

This paper presents particle swarm optimization approach for designing Rotary Inverted Pendulum (RIP) controller. The goal is to balance the pendulum in the inverted position. The intuition of this work is using state-feedback controller as the stabilizer component of the system whereas in other existing systems, only Proportional-Integral-Derivative (PID) is used for that purpose. Simulation results demonstrate effectiveness of the proposed controller design method and can be considered as promising ways for control of various similar nonlinear systems.

关键词： Optimization Control RIP PSO State-Feedback parametric subharmonic oscillator Controllers State feedback INVERTED PENDULUM controller design Standing Orders ripping particle swarm algorithm particle swarm optimization algorithms Clock weights

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