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检索条件"主题词=Particle Swarm optimization algorithm"

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An Improved PSO-BP Neural Network and Its Application to Earthquake Prediction 28

An Improved PSO-BP Neural Network and Its Application to Ear...

引用

28th Chinese Control and Decision Conference

作者： Li, Cao Liu, Xiaoyu Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China

ISBN: (纸本)9781467397148

This paper presents a way of combining BP (Back Propagation) neural network and an improved PSO (particle swarm optimization) algorithm to predict the earthquake magnitude. It is known that the BP neural network and the normal PSO-BP neural network have some defeats, such as the slow convergence rate, easily falling into local minimum values. For improving the properties of PSO, some proposed the linear decreasing inertia weight strategy. Furthermore, this paper uses a nonlinear decreasing inertia weight in PSO to get a faster training speed and better optimal solutions. Compared with the linear decreasing strategy, the inertia weight in our nonlinear method has a faster declining speed in the early iteration, which can enhance the searching precision. In the late iteration, the inertia weight has a slower declining speed to avoid trapping in local minimum value. Then we apply the improved PSO to optimize the parameters of BP neural network. In the end, the improved PSO-BP neural network is applied to earthquake prediction. The simulation results show that the proposed improved PSO-BP neural network has faster convergence rate and better predictive effect than the BP neural network and the normal PSO-BP neural network.

关键词： BP neural network particle swarm optimization algorithm Inertia Weight Earthquake Prediction

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A Method Based on PSO for Pipe Routing Design

A Method Based on PSO for Pipe Routing Design

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6th lEEE Annual International Conference on Cyber Technalogy in Automation, Control and Intelligent Systems (IEEE-CYBER)

作者： Wang Changtao Sun Xiaotong Sun Liangliang Yuan Tiancheng Shenyang Jianzhu Univ Sch Informat & Control Engn Shenyang Peoples R China

ISBN: (纸本)9781509027323

The optimization of pipe path planning plays an important role in a building system. The three-dimensional building environment model is established, because of complicated building pipeline systems. Also this paper proposed a new method of pipe routing layout-design a quick method of generating orthogonal path. particle swarm optimization (PSO) is then used for optimizing the pipe path with a fixed-length code for the path, and for it's vulnerable to local problems, a mutation operator is adding to increase the diversity of the population. Finally, simulation results are presented and prove the feasibility and effectiveness of this method.

关键词： pipe path planning three-dimensional model particle swarm optimization algorithm fixed-length code

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An Improved particle swarm optimization Based on Adaptive Mutation and P Systems for Micro-grid Economic Operation

An Improved Particle Swarm Optimization Based on Adaptive Mu...

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Chinese Intelligent Automation Conference (CIAC)

作者： Sun, Zhang Liu, Tao Wang, Jun Luo, Juan Li, Hong Xihua Univ Sch Elect & Informat Engn 999 Jinzhou Rd Chengdu Sichuan Peoples R China

ISBN: (纸本)9783662464694;9783662464687

A particle swarm optimization (PSO) algorithm based on adaptive mutation and P systems is proposed to overcome trapping in local optimum solution and low optimization precision in this paper. The algorithm combines the evolutionary rules of PSO, the strategy of adaptive mutation with the hierarchical membrane structure, and communication rules of P systems. At the same time, in order to achieve rapid economic operation and effectiveness of the micro-grid the proposed algorithm is investigated in experiments which are based on the function optimization of micro-grid's economic operation. Furthermore, the feasibility and effectiveness of the proposed algorithm are shown in the experimental results.

关键词： particle swarm optimization algorithm P systems Function optimization Economic operation

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Study on Network Flow Prediction Model Based on particle swarm optimization algorithm and RBF Neural Network

Study on Network Flow Prediction Model Based on Particle Swa...

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

作者： Zhang Yu Bin HeBei Vocational Art College ShiJiaZhuang,China Lin Li Zhong ShiJiaZhuang College ShiJiaZhuang ,China Zhang Ya Ming ShiJiaZhuang Information Engineering Vocational College ShiJiaZhuang,China

It is significant to control network congestion by time series forecasting research for network flow. The hybrid method of particle swarm optimization algorithm and RBF neural network is applied to predict network flow and gain the desirable network flow prediction results. In the hybrid method, particle swarm optimization algorithm is selected and adjusted to the connection weights and the center of radial basis function and the width of radial basis function. The network flow data are collected to search the prediction ability of particle swarm optimization algorithm and RBF neural network. Compared with the results of RBF neural network and BP neural network, particle swarm optimization algorithm and RBF neural network has better forecasting performance.

关键词： network flow neural network particle swarm optimization algorithm prediction model

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swarm Intelligence algorithm for Job Scheduling in Computational Grid 7

Swarm Intelligence Algorithm for Job Scheduling in Computati...

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7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS)

作者： Effatparvar, Mehdi Aghayi, Somayeh Asadzadeh, Vahid Dashti, Yosef Islamic Azad Univ Ardabil Branch Dept Comp Ardebil Iran Islamic Azad Univ Ardabil Branch Sama Tech & Vocat Training Coll Ardebil Iran Islamic Azad Univ Germi Branch Dept Comp Germi Iran

ISBN: (纸本)9781509006656

Grid computing is actually the next generation of distributed systems. Its objective is to create a powerful, large and autonomous virtual computer. This computer is created through assembling countless heterogeneous resources with the aim of sharing them. Scheduling is one of the most important and challenging issue of such systems. An accurate and efficient schedule is required to increase the grid efficiency. Grid resources belong to different management domains;each one applies different management policies. This paper proposed a new heuristic approach based on particle swarm optimization algorithm in order to scheduling the jobs in gird environment. The proposed algorithm would create an optimal scheduler to complete the jobs in the minimum flowtime and makespan.

关键词： Job Scheduling Computational Grid particle swarm optimization algorithm Flowtime Makespan

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Frequency Stability of Microgrids Based on H∞ Methods 35

Frequency Stability of Microgrids Based on H∞ Methods

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第35届中国控制会议

作者： ZHAO Jian WANG ChaoLi School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology

ISBN: (纸本)9781509009107

The widespread use of renewable energy in microgrid power systems would cause frequency *** in this work,is the frequency stability of the microgrid consisting of wind power systems,photovoltaic systems,micro-turbines,electrolyzers and fuel cells with the variation of load power.A novel robust controller is designed for the microgrid to suppress frequency fluctuation by tuning the output of the micro-turbine,electrolyzer and fuel *** generation-rate constraint of the micro-turbine is *** integrating the switching states of the fuel cell and electrolyzer with the micro-turbine power output in order to prevent too large changing rate of the micro-turbine power and improve the utilization of renewable *** is well known that the key point of design a H∞ mixed sensitivity controller is how to select appropriate weighting ***,there are no effective rules to do *** this reason,the particle swarm optimization（PSO） algorithm is used to optimize the weighting functions to enable the system to achieve optimal *** results show that the mentioned frequency control strategy can effectively stabilize the frequency fluctuation and improve the utilization of renewable energy,and is still effective for the case of large-scale renewable energy penetration.

关键词： microgrid frequency control H∞ control particle swarm optimization algorithm weighting functions

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optimization and CONTROL of a HEV

Optimization and CONTROL of a HEV

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International Conference on Electrical, Electronics, and optimization Techniques (ICEEOT)

作者： Farahat, Mohammad Esmaeil Farahat, Said Islamic Azad Univ Aliabad Katoul Branch Dept Elect Engn Aliabad Katoul Iran Univ Sistan & Baluchestan Dept Mech Engn Zahedan Iran

ISBN: (纸本)9781467399395

Vehicles play an important role in transportation. Using fossil fuels as their source of energy, they are a source of pollution. HEVs are improving and a day will come that they will take over the role of conventional vehicles. In the path of improving HEVs, optimizing components sizes of the vehicle and better strategy controls are vital methods to reach the desired goals. In this path practical tools for optimization are evolutionary algorithms and fuzzy controllers as a strategy control decision maker. In this paper, accompanied with these tools extraordinary improvements are made. On the UDDS cycle using PSO algorithm we reached 57%, 68%, 34% and 8% decrease respectively in fuel consumption, emission, economic and acceleration and we reached 30% increase in maximum speed gained. On the HWFET cycle we reached 74%, 79%, 34% and 8% decrease respectively in fuel consumption, emission, economic and acceleration and we reached 30% increase in maximum speed gained. Acquiring a Pareto Front for two fitness functions we can choose a specific vehicle with special capabilities according to our priorities.

关键词： Hybrid vehicles particle swarm optimization algorithm power management strategy sizing drive cycle

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Short-Term Traffic Flow Prediction Method Based on Grey Adaptive particle swarm Support Vector Machine 2

Short-Term Traffic Flow Prediction Method Based on Grey Adap...

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2nd IEEE International Conference on Computer and Communications (ICCC)

作者： Duo, Mei Xu, E. Qi, Yan Bohai Univ Coll Informat Sci & Technol Jinzhou Peoples R China

ISBN: (纸本)9781467390262

In order to further improve the accuracy of the short-term traffic flow prediction, a combination of short-term traffic flow prediction model had been proposed by analyzing the characteristics of grey model, adaptive particle swarm optimization (PSO) algorithm and support vector machine (SVM) model. First, use the grey model to accumulate the original traffic flow data, weaken the randomness of the traffic flow data sequence, then optimize the support vector machine model based on adaptive particle swarm optimization algorithm and realize short-term traffic flow prediction, finally, get the final predicted value table by grey mode. The model was verified based on the traffic flow data of the major road in Changchun and the experimental result showed the proposed model was effective and feasible.

关键词： traffic flow prediction grey model particle swarm optimization algorithm support vector machine

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Optimal location and sizing of STATCOM using Fuzzy-PSO approach 6

Optimal location and sizing of STATCOM using Fuzzy-PSO appro...

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6th IEEE International Conference on Power Systems (ICPS)

作者： Rana, Bharat Singh Srivastava, Laxmi Madhav Inst Sci & Technol Dept Elect Engn Gwalior India

ISBN: (纸本)9781509001286

In this paper, an evolutionary computing technique comprising of Fuzzy logic and particle swarm optimization (PSO) algorithm has been proposed for optimal location and sizing of STATCOM to improve the voltage stability and to minimize the total voltage deviation in a power system. The proposed Fuzzy-PSO algorithm has been applied using two steps. As the first step, the weakest buses selected for placement of STATCOM using modal analysis. In the second step Fuzzy-PSO algorithm has been applied for selecting optimal location of STATCOM considering these selected weakest buses. The proposed approach has been applied on the standard IEEE 30-bus system considering the most severe single line outage contingencies under normal loading and under stressed condition. The proposed Fuzzy-PSO approach has been found to be quite satisfactory.

关键词： fuzzy performance index modal analysis particle swarm optimization algorithm STATCOM voltage stability

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A Design Method of Zero Ripple Input Current DC-DC Converter Assisted by Constrained GA&PSO algorithms to Minimize Power Losses 19

A Design Method of Zero Ripple Input Current DC-DC Converter...

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19th International Conference on Electrical Machines and Systems (ICEMS)

作者： Marsala, Giuseppe Ragusa, Antonella CNR ISSIA UOS Palermo Italy

ISBN: (纸本)9784886860989

The paper presents a constrained optimization procedure to design a DC-DC converter with coupled inductors for minimizing the power losses. Two algorithms have been used, Genetic and particle swarm optimization algorithm, and the results have been compared. In particular, with the proposed technique, the electrical, magnetic and geometrical characteristics of the coupled inductors have been obtained and the values of duty cycle and frequency have been defined in order to obtain the maximum efficiency.

关键词： DC-DC converter Genetic algorithm particle swarm optimization algorithm power losses

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