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检索条件"主题词=particle swarm algorithm"

共 736 条记录，以下是191-200 订阅

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Multi-Objective Reactive Power Optimization Based on Chaos particle swarm Optimization algorithm

Multi-Objective Reactive Power Optimization Based on Chaos P...

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2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA)

作者： He Xiao Pang Xia Zhu Da-rui Liu Chong-xin Xi An Jiao Tong Univ Sch Elect Engn Xian Shaanxi Peoples R China

ISBN: (纸本)9781479927166

Reactive power optimization is closely related to voltage quality, network loss and it has great significance for the safety, reliability and economical operation of the power system. For shortage of traditional reactive power optimization, this paper establishes a multiple-objective reactive power optimization model which consists of minimum active power loss, minimum node voltage deviation, best static voltage stability and minimum reactive cost. To optimize four targets simultaneously, this paper has proposed a multi-objective reactive power optimization method which applies the chaotic particle swarm optimization algorithm based on Pareto solutions and finds the Pareto optimal solution sets of multi-objective optimization problems, then policy makers can make a scientific decision according to the actual situation. To prove the validity of the method proposed, this paper makes a multiple-objective reactive power optimization analysis for the IEEE30-bus system. The result shows that the method presented in this paper can achieve good results of reactive power optimization for decision makers to refer to.

关键词： Reactive power optimization Chaos optimized particle swarm algorithm Pareto solutions Multi-objective optimization

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The Research of Parallel Multi-objective particle swarm Optimization algorithm

The Research of Parallel Multi-objective Particle Swarm Opti...

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2014 IEEE 5th International Conference on Software Engineering and Service Science

作者： WU Jian TANG XinHua CAO Yong Key Laboratory of Evidence-Identifying in Universities of Shandong(Shandong University of Political Science and Law) Department of Information Science and Technology Shandong University of Political Science and Law

The shortcomings of traditional serial algorithm on the multi-objective optimization problems are well known for its long computation time and the slow convergence rate,especially when we have complicated computation and large amount of *** conquer these shortcomings,we propose a parallel multi-objective particle swarm optimization *** analyzing the mechanism of multiobjective particle swarm optimization algorithm,we introduced the parallel mechanism into the multi-objective particle swarm algorithm,and realized a parallel multiobjective particle swarm algorithm based on the model of the *** apply our algorithm on the knapsack problem as an illustration,and find the solving efficiency of the multiobjective problems improves evidently.

关键词： particle swarm algorithm parallel multiobjective optimization

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Multi-Core Real-Time Scheduling algorithm Based on particle swarm Optimization algorithm

Multi-Core Real-Time Scheduling Algorithm Based on Particle ...

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2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)

作者： Xingzhi Liu Yan Zeng Wenli Chen Yu Su Rui Wang State Grid Chongqing Electric Power Company Marketing Service Center

ISBN: (纸本)9781665417358

Priority-based scheduling algorithms have a wide range of applications in real-time *** today' s commonly used task scheduling algorithms,only the shortest scheduling time is used as the only criterion,while ignoring the importance of task *** allocation among multiple cores is also difficult to *** this time,traditional priority scheduling shows great *** order to build a preemptive priority scheduling algorithm for load balancing among multiple cores,this paper first studies the scheduling principles of particle swarm algorithm and annealing algorithm among CPU nodes,and simulates the traditional scheduling algorithm that may appear before the optimal solution for scheduling is ***,and then extract the mathematical model of operating system *** on the particle swarm algorithm and combined with the scheduling advantages of other heuristic algorithms,an optimized preemptive priority scheduling algorithm based on particle swarm algorithm is proposed,which completes multitasking among multiple *** on the priority of the process,the process is assigned to a more suitable processor core,which improves the efficiency of priority scheduling of heterogeneous multi-core operating ***,the effectiveness of the algorithm is verified by simulation experiments.

关键词： particle swarm algorithm Simulated annealing real-time scheduling priority heterogeneous multi-core

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Application of Wavelet Neural Network with particle swarm Optimization algorithm in Boiler Faults Diagnosis

Application of Wavelet Neural Network with Particle Swarm Op...

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2016 6th International Conference on Machinery,Materials,Environment,Biotechnology and Computer(MMEBC 2016)

作者： Yun Du Yadong Liu Department of Electrical Engineering and Automation Hebei University of Science and Technology

In view of the main fault type in boiler steam water system, a variety of complex fault data are extractedA wavelet neural network fault diagnosis based on particle swarm optimization algorithm is designedThe wavelet neural network constructed by three-layer wavelet neural network, is trained by particle swarm algorithmBy optimizing the weights factor, scale factor and shift factor wavelet neural network on particle swarm algorithm, the training speed of wavelet neural network is accelerated and the training accuracy is also improvedThe simulation results show that the improved wavelet neural network algorithm is applied to the fault diagnosis of boiler, which can effectively eliminate the influence of redundant connection structure on network diagnostic ability and provide a new way for the boiler fault diagnosis.

关键词： particle swarm algorithm Wavelet Neural Network Faults Diagnosis

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Design a PLL controller using a particle swarm Optimization algorithm

Design a PLL controller using a Particle Swarm Optimization ...

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IEEE International Congress on Electronics, Electrical Engineering and Computing (INTERCON)

作者： Lucas Lima Rodrigues Ana Beatriz Alvarez Omar A. Chura Vilcanqui Huilman Sanca Sanca Exact and Technological Sciences Center Federal Universisty of Acre Rio Branco Brazil Exact and Technological Sciences Center Federal Universisty of Recôncavo da Bahia Cruz das Almas Brazil

ISBN: (数字)9781728193779

ISBN: (纸本)9781728193786

In this paper a design methodology for the controller of a Synchronous Rotating Reference Frame Phased-Locked Loop (SRF-PPL) using the particle swarm Optimization (PSO) algorithm is presented. Subsequently, the proposed method is compared with a classic method in the literature. Simulation results shows that the proposed method is faster and efficient than the classical approach. Furthermore, this method can be easily adapted to others PLLs.

关键词： Synchronous Rotating Reference Frame Phase-Locked Loop (SRF-PLL) phase detection particle swarm Optimization (PSO) controller design Phase detection particle swarm optimization controller design particle swarm algorithm Controllers Prolymphocytic Leukemia Phase locked loops

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Improvement of Inertia Weight Declining Strategy Based on particle swarm Optimization algorithm

Improvement of Inertia Weight Declining Strategy Based on Pa...

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作者： Li-li GUO Yong-LIU Wei-xi WANG Ya-wei LI Hao-nan LI Jia-qi WANG Zi-wei LIU Hua-wei SUN College of Electronic Engineering Heilongjiang University Heilongjiang Eastern Water Saving Equipment Co. Ltd.

The standard particle swarm optimization algorithm introduces the inertia weighet w, which becomes a calculation method for finding the extremum of the function effectively. It is easy to converge fast. Nowadays, the linear decreasing dynamic, inertia weighting strategy is widely used. Although this improvement is very successful. However, the search process is a nonlinear and complex process. In order to better maintain the balance between global and local search capabilities, this paper proposes a nonlinear decreasing dynamic inertia weighting strategy based on the linear decreasing strategy, using Griewank, Rastrigrin, Sphere, The simulation experiments of four standard test functions of JDSchaffer were carried out, and the inertia weights in the basic particle swarm optimization algorithm were compared with the nonlinear decrement of fixed weight w=95.0, linear decreasing LDIW and Chen[6] exponential curve respectively. The improved nonlinear weight decrement strategy is superior to other algorithms in terms of convergence speed, convergence precision number of iterations.

关键词： particle swarm algorithm Inertial weight Nonlinear decrease

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Research on FJSP based on CUDA parallel cellular particle swarm optimization algorithm

Research on FJSP based on CUDA parallel cellular particle sw...

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International Conference on Software Intelligence Technologies and Applications & International Conference on Frontiers of Internet of Things 2014

作者： Liu Shenghui Zhang Shuli School of Software Harbin University of Science and Technology China

This paper raised a Cellular particle swarm optimization, (CPSO) algorithm based on GPU parallel, which could reduce the computing time for processing amounts of data and solving Flexible job shop scheduling problem(FJSP) complex problems. The implementation of proposed algorithm combines the GPU parallel technology and traditional CPSO algorithm. The method accelerates the convergence rate of the particle swarm by using a large amounts of GPU threads to process each particle. In the experimental part, we use the benchmark data to test the performance of proposed algorithm. Comparing with serial CPSO, the proposed method can achieve higher computing speed ratio under the premise that ensure the optimal solution.

关键词： CUDA Cellular particle swarm parallel computing particle swarm optimization PARALLEL PROCESSING (COMPUTERS) GRAPPER PICK UP Graphics Processing Unit algorithms Parallel Lines particle swarm algorithm PREMISES optimal solution Cells

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Stability and l~(2) Gain Analysis for the particle swarm Optimization algorithm

Stability and l~(2) Gain Analysis for the Particle Swarm Opt...

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2009 American Control Conference (ACC 2009)

作者： Yuji Wakasa Kanya Tanaka Takuya Akashi Graduate School of Science and Engineering Yamaguchi University 2-16-1 Tokiwadai Ube Yamaguchi 755-8611 Japan

ISBN: (纸本)9781424445233

Stability of the particle swarm optimization algorithm is analyzed without any simplifying assumptions made in the previous works. To evaluate the convergence speed of the algorithm, the decay rate is introduced, and a method for finding the largest lower bound of the decay rate is presented. Moreover, it is pointed out that the l~(2) gain of the algorithm can be used to measure exploration ability of the algorithm, and a method for finding of the smallest upper bound of the l~(2) gain is provided. The above methods are based on linear matrix inequality techniques and therefore are carried out efficiently by using convex optimization tools. Numerical examples are given to show that the analysis methods are reasonable and effective to select the parameters in the algorithm.

关键词： particle swarm algorithm particle swarm optimization algorithms Gain linear matrix inequalities decay rates FINDINGS HIGH-SPEED algorithm Decay Decay Decay decay Decay Disintegration biodeterioration

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particle swarm optimization algorithm based on divided-interval chaotic search

Particle swarm optimization algorithm based on divided-inter...

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Bio-Inspired Models of Network, Information and Computing Systems, BIONETICS

作者： Qiushi Xu Xiangdong Wang Shenyang University of Technology Shenyang China

ISBN: (纸本)9781424436491;9789639799059

Though chaotic particle swarm optimization algorithm lets particles search in the whole variable space, the search scale is too large and the high precision of solution is hard to achieve. This paper proposes a particle swarm optimization algorithm based on divide-interval chaotic search, it lets the particles search in the selected interval, reduces the scope of the search space, and makes the solution more approximate to the global optimum. And the comparable experiment shows that the algorithm has preferable results.

关键词： Chaos particle swarm optimization Divided-interval particle swarm optimization chaos particle swarm algorithm global optimum Searches algorithms

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Can Tournament Selection Improve Performances of the Classical particle swarm Optimization algorithm?

Can Tournament Selection Improve Performances of the Classic...

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European Conference on Antennas and Propagation

作者： Ruzica Golubovic Ivica Stevanovic Dragan Olcan Juan Mosig Laboratory of Electromagnetics and Acoustics Ecole Polytechnique Federale de Lausanne Freescale Semiconductor University of Belgrade School of Electrical Engineering

ISBN: (纸本)9783800731527

particle swarm Optimization (PSO) algorithm is known to be very efficient solution for electromagnetic (EM) optimization problems. In this paper we show that binary tournament selection applied to PSO algorithm further speeds-up its convergence. Having in mind that EM simulation is the most time-consuming part of the optimization, reducing the overal number of iterations (EM solver calls) is of a paramount relevance.

关键词： Tournament particle swarm optimization particle swarm algorithm parametric subharmonic oscillator algorithms Converge

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