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

共 1416 条记录，以下是181-190 订阅

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Utilization Efficiency Forecasting of Moisture Content in Maize Based on particle swarm optimization algorithm and RBF Neural Network

Utilization Efficiency Forecasting of Moisture Content in Ma...

引用

2nd International Conference on Computer and Automation Engineering (ICCAE)

作者： Yu Xiaofang Gao Jvlin Song Guodong Inner Mongol Agr Univ Hohhot 010010 Peoples R China Inner Mongolia Autonomous Reg Seed Control Stn Hohhot 010010 Peoples R China

ISBN: (纸本)9781424455690

Utilization efficiency forecasting of moisture content in maize has a great importance to maize production. RBF neural network is able to universal approximation. PSO-RBF neural network which combines particle swarm optimization (PSO) with RBF neural network is proposed to utilization efficiency forecasting of moisture content in maize. Maize fields of the farms in Henan province are applied to study the utilization efficiency forecasting ability of moisture content in maize by the proposed PSO-RBF neural network method. And BP neural network and normal RBF neural network are applied to compare the PSO-RBF neural network method. By analyzing the experimental results, it is indicated that utilization efficiency forecasting ability of moisture content in maize by PSO-RBF neural network than that by RBF neural network and BP neural network.

关键词： utilization efficiency moisture content RBF neural network particle swarm optimization algorithm

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An Improved particle swarm optimization algorithm

An Improved Particle Swarm Optimization Algorithm

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International Forum on Materials Analysis and Testing Technology (IFMATT 2013)

作者： Ni, Hongmei Wang, Weigang Northeast Petr Univ Comp & Informat Technol Coll Daqing Peoples R China Northeast Petr Univ Mech Sci & Engn Coll Daqing Peoples R China

ISBN: (纸本)9783037859537

Niche is an important technique for multi-peak function optimization. When the particle swarm optimization (PSO) algorithm is used in multi-peak function optimization, there exist some problems, such as easily falling into prematurely, having slow convergence rate and so on. To solve above problems, an improved PSO algorithm based on niche technique is brought forward. PSO algorithm utilizes properties of swarm behavior to solve optimization problems rapidly. Niche techniques have the ability to locate multiple solutions in multimodal domains. The improved PSO algorithm not only has the efficient parallelism but also increases the diversity of population because of the niche technique. The simulation result shows that the new algorithm is prior to traditional PSO algorithm, having stronger adaptability and convergence, solving better the question on multi-peak function optimization.

关键词： particle swarm optimization algorithm Multi-peak function optimization Niche

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Using particle swarm optimization algorithm in Multimedia CDN Content Placement

Using Particle Swarm Optimization Algorithm in Multimedia CD...

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5th International Symposium on Parallel Architectures, algorithms and Programming (PAAP)

作者： Chen, Jian-Bo Chen, Chu-Chuan Ming Chuan Univ Dept Informat & Telecommun Engn Tao Yuan Taiwan

ISBN: (纸本)9780769548982;9781467345668

Multimedia Content Delivery Networks (CDN) is used to improve the performance and reliability on Internet. In CDN architecture, the multimedia contents are replicated from the origin server to replica servers in order to improve the performance and minimize the use of network bandwidth. Efficient placing the multimedia contents in CDN is a challenging problem. There are five factors that can be used to determine the placement of multimedia contents, they are bandwidth availability, connection availability, storage availability, CPU availability, and memory availability. In this paper, a particle swarm optimization (PSO) algorithm is adopted to solve this issue. PSO algorithm uses these five different input parameters as different dimensions. In this five dimension searching space, PSO algorithm can find out the global optimal solution. With this global optimal solution, it is the most appropriate replica server that must place the multimedia content. The simulation results show that the PSO algorithm can achieve a better performance than other algorithms.

关键词： component CDN Content Placement particle swarm optimization algorithm

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A Novel Quantum-behaved particle swarm optimization algorithm 14

A Novel Quantum-behaved Particle Swarm Optimization Algorith...

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International Symposium Distributed Computing and Applications Business Engineering and Science

作者： Zhao, Jing Liu, Hong Qilu Univ Technol Sch Informat Jinan Shandong Peoples R China Shandong Normal Univ Shandong Prov Key Lab Distributed Comp Software Jinan Peoples R China Shandong Normal Univ Shandong Prov Key Lab Distributed Comp Software Coll Informat Sci & Engn Jinan Peoples R China

ISBN: (纸本)9781467365932

A novel Quantum-behaved particle swarm optimization algorithm with probability (P-QPSO) is introduced to improve the global convergence property of QPSO. In the proposed algorithm, all the particles keep the original evolution with large probability, and do not update the position of particles with small probability, and re-initialize the position of particles with small probability. Seven benchmark functions are used to test the performance of P-QPSO. The results of experiment show that the proposed technique can increase diversity of population and converge more rapidly than other evolutionary computation methods.

关键词： particle swarm optimization algorithm quantum-behaved probability benchmark function

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Cellular particle swarm optimization algorithm for Logistics Distribution Locating Problem

Cellular Particle Swarm Optimization Algorithm for Logistics...

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International Seminar on Future BioMedical Information Engineering

作者： Liu, Yi Hangzhou Dianzi Univ Inst Management Sci & Informat Engn Hangzhou 310018 Zhejiang Peoples R China

ISBN: (纸本)9780769535616

Logistics distribution locating problem is an important area in Logistics, which select the most reasonable location of distribution centers from many places. This paper establish the Cellular PSO algorithm, which combine the particle swarm optimization algorithm and cellular automata. This algorithm was tested in the simulation experiment, and the result indicate that the Cellular PSO algorithm is a effective method of solving the problem of choosing the distribution centers location which can overcome the low precision of the basic particle swarm optimization algorithm. In additional, the Cellular PSO algorithm has high quality and efficiency of searching.

关键词： Cellular Automata particle swarm optimization algorithm Logistics Distribution Centers Location

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ARIMA Model Estimated by particle swarm optimization algorithm for Consumer Price Index Forecasting

ARIMA Model Estimated by Particle Swarm Optimization Algorit...

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International Conference on Artificial Intelligence and Computational Intelligence

作者： Wang, Hongjie Zhao, Weigang Lanzhou Jiaotong Univ Railway Tech Coll Lanzhou 730000 Peoples R China

ISBN: (纸本)9783642052521

This paper presents an ARIMA model which uses particle swarm optimization algorithm (PSO) for model estimation. Because the traditional estimation method is complex and may obtain very bad results, PSO which can be implemented with ease and has a powerful optimizing performance is employed to optimize the coefficients of AMNIA. In recent years, inflation and deflation plague the world moreover the consumer price index (CPI) which is a measure of the average price of consumer goods and services purchased by households is usually observed as an important indicator of the level of inflation, so the forecast of CPI has been focused on by both scientific community and relevant authorities. Furthermore, taking the forecast of CPI as a case, we illustrate the improvement of accuracy and efficiency of the new method and the result shows it is predominant in forecasting.

关键词： ARIMA model particle swarm optimization algorithm Moment estimation Consumer price index

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Application of Optimized Neural Network Based on particle swarm optimization algorithm in Fault Diagnosis

Application of Optimized Neural Network Based on Particle Sw...

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8th IEEE International Conference on Cognitive Informatics (ICCI 2009)

作者： Zhong, Bingxiang Wang, Debiao Li, Taifu Chongqing Univ Sci & Technol Coll Elect Informat Engn Chongqing 401331 Peoples R China

ISBN: (纸本)9781424446421

In this paperan algorithm based on particle swarm optimization algorithm for RBF neural network is propose. With particle swarm optimization algorithm, neural network weights are optimized. Also through the dynamic regulation of the number of radial basis function in neural network hidden layer, neural network structure is optimized. The algorithm is applied to gearbox fault diagnosis. Experimental results show the effectiveness and great performance. Classification effect of neural network based on particle swarm optimization algorithm is better than that of the RBF neural network for identifying effectively the different status of gearbox and monitoring timely the status changes of gearbox. Also it can reduce the time for fault diagnosis and improve accuracy of fault diagnosis.

关键词： particle swarm optimization algorithm RBF neural networks Fault diagnosis Gearbox

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A new algorithm based on the Gbest of particle swarm optimization algorithm to improve Estimation of Distribution algorithm

A new Algorithm based on the Gbest of Particle Swarm Optimiz...

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International Conference on Smart Computing and Electronic Enterprise (ICSCEE)

作者： Zhao, Qiuyue Gao, Ying Guangzhou Univ Comp Sci & Educ Software Inst Guangzhou Peoples R China

ISBN: (纸本)9781538648384

In recent years, with the rise of artificial intelligence and deep learning, as an evolutionary algorithm based on probability model, estimation of distribution algorithm has been widely research and development. The estimation of distribution algorithm without the traditional genetic operation such as crossover and mutation, is a new kind of evolution model. As an algorithm based on probabilistic mode, the estimation of distribution algorithm establishes a probabilistic model describing the solution space of optimization problems. With the emergence for big data, the convergence of the algorithm and the requirements for solving precision are also increasing. This paper attempts to improve the distribution estimation algorithm. The optimal population of each iteration is found through the location update of each iteration of the particle swarm optimization (PSO) algorithm. The simulation test was carried out with ten benchmark test function. The proposed algorithm was compared with the GA_EDA9improved genetic algorithm) and the basic distribution estimation (EDA) algorithm. Experimental results show that the new algorithm is superior to GA_EDA and basic EDA in terms of convergence and accuracy.

关键词： Estimation of distribution algorithm particle swarm optimization algorithm simulation

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Vehicle path planning method based on particle swarm optimization algorithm

Vehicle path planning method based on particle swarm optimiz...

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3rd International Conference on Manufacturing Science and Engineering (ICMSE 2012)

作者： Yu, Sheng-long Bo, Yu-ming Chen, Zhi-min Zhu, Kai Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Jiangsu Peoples R China

ISBN: (纸本)9783037853696

A particle swarm optimization algorithm (PSO) is presented for vehicle path planning in the paper. particle swarm optimization proposed by Kennedy and Eberhart is derived from the social behavior of the birds foraging. particle swarm optimization algorithm a kind of swarm-based optimization *** simulation experiments performed in this study show the better vehicle path planning ability of PSO than that of adaptive genetic algorithm and genetic algorithm. The experimental results show that the vehicle path planning by using PSO algorithm has the least cost and it is indicated that PSO algorithm has more excellent vehicle path planning ability than adaptive genetic algorithm,genetic algorithm.

关键词： particle swarm optimization algorithm genetic algorithm vehicle path planning

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Cloud Task Scheduling Based on Chaotic particle swarm optimization algorithm

Cloud Task Scheduling Based on Chaotic Particle Swarm Optimi...

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International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS)

作者： Li Yingqiu Li Shuhua Gao Shoubo Dalian Neusoft Inst Informat Dept Software Engn Dalian 116023 Liaoning Peoples R China

ISBN: (纸本)9781509060610

In cloud computing environment, there is a large quantity of submitted tasks by users. How to schedule these massive tasks efficiently and reasonably becomes a serious challenge. This paper proposes a Chaotic particle swarm optimization algorithm (CPSO) to overcome the problems of Standard particle swarm algorithm such as premature convergence and low accuracy. Firstly, in initial process, chaotic sequence is introduced to enhance the diversity of particles. Then, an effective diagnosis mechanism of premature is adopted to determine local convergence and algorithm correction is performed by chaotic mutation, which could activate the particles in stagnation and make them escape from local optimum. Simulation experiments show that the proposed approach is feasible and effective.

关键词： Cloud Computing Task Scheduling particle swarm optimization algorithm Chaotic Sequence Chaotic Mutation

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