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

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The Application of An Improved Cultural algorithm in Grid Computing

The Application of An Improved Cultural Algorithm in Grid Co...

引用

第25届中国控制与决策会议

作者： ZHOU Wei BU Yan-ping ZHOU Ye-qing School of Business East China University of Science and Technology School of Technology Shanghai Jiaotong University School of Mathematical sciences Fudan University

ISBN: (纸本)9781467355339

A cultural algorithm, rooting from simulation of evolution of human being society, provides a new computable framework of evolution algorithms. A novel cultural algorithm based on particle swarm optimization （PSO） algorithm was proposed in this paper. With the advent of the grid, task scheduling in heterogeneous environments becomes more and more important. After analyzing the model of grid scheduling problem, the CPSO algorithm was presented to solve the resource scheduling problem in grid computing. The optimal objective is to minimize the total completing time. The improved algorithm can keep all the advantages of the standard PSO, such as implementation simplicity, low computational burden, and few control parameters, etc. Simulation results demonstrate that it can be superior to the regular PSO. We also tested the CPSO algorithm with the Max-Min method to show the algorithm’s efficiency.

关键词： cultural algorithm particle swarm optimization algorithm grid task scheduling

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A Hybrid PSO/GA algorithm for Job Shop Scheduling Problem 1

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1st International Conference on swarm Intelligence

作者： Tang, Jianchao Zhang, Guoji Lin, Binbin Zhang, Bixi South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Guangdong Peoples R China South Chain Normal Univ Comp Sch Guangzhou 510631 Peoples R China South China Univ Technol Coll Sci Guangzhou 510641 Guangdong Peoples R China Guangdong Univ Technol Sch Econ Management Guangzhou 510090 Peoples R China

ISBN: (数字)9783642134951

ISBN: (纸本)9783642134944

The job shop scheduling problem is a well-known NI) hard problem, on which genetic algorithm is widely used However. due to the lack of the major evolution direction. the effectiveness of the regular genetic algorithm is restricted In this paper. we propose a new hybrid genetic algorithm to solve the job shop scheduling problem The particle swarm optimization algorithm is introduced to get the initial population, and evolutionary genetic operations are proposed We validate the new method on seven benchmark datasets. and the comparisons with some existing methods verify as effectiveness

关键词： particle swarm optimization algorithm hybrid genetic algorithm Job shop scheduling problem

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Intelligent Sign Language Recognition Using Enhanced Fourier Descriptor: A Case of Hausa Sign Language 2

Intelligent Sign Language Recognition Using Enhanced Fourier...

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2nd IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)

作者： Hassan, S. T. Abolarinwa, J. A. Alenoghena, C. O. Bala, S. A. David, M. Farzaminia, Ali Fed Univ Technol Minna Dept Telecommun Engn Minna Nigeria Univ Malaysia Sabah Fac Engn Kota Kinabalu Malaysia

ISBN: (纸本)9781538608463

Hausa sign language (HSL) is the main communication medium among deaf-mute Hausas in northern Nigeria. HSL is so unique that a deaf-mute individual from other part of the country can rarely understand it. HSL includes static and dynamic hand gesture recognitions. In this paper we present an intelligent recognition of static, manual and non-manual HSL using an enhanced Fourier descriptor. A Red Green Blue (RGB) digital camera was used for image acquisition and Fourier descriptor was used for features extraction. The features extracted chosen manually and fed into artificial neural network (ANN) which was used for classification. Thereafter particle swarm optimization algorithm (PSO) was used to optimize the features based on their fitness in order to obtain high recognition accuracy. The optimized features selected gave a higher recognition accuracy of 90.5% compared to the manually selected features that gave 74.8% accuracy. High average recognition accuracy was achieved;hence, intelligent recognition of HSL was successful.

关键词： Hausa Sign Language Fourier Descriptor particle swarm optimization algorithm Artificial Neural Network

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Feedback Controller Design for Chemotherapy Drug Scheduling via PSO optimization algorithm

Feedback Controller Design for Chemotherapy Drug Scheduling ...

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World Congress on Engineering and Computer Science

作者： Baghernia, P. Moghaddam, R. K. Islamic Azad Univ Mashhad Branch Dept Elect Engn Mashhad Iran

ISBN: (纸本)9789881925244

Cancer is related to a class of diseases characterized by out-of-control cell growth. Chemotherapy as one of the most conventional methods of cancer treatment aims to kill cancer cells, but this treatment will damage healthy cells as well. In this regard, mathematical modeling and optimization of drug scheduling can be effective in improving the drug injection timing, with minimum side effects. In this paper a phase specific cancer tumor model have been considered to describe the effect of drug on different cell populations, plasma drug concentration and toxic side effects. A feedback controller of PID type is developed in order to maintain a predefined drug concentration level. This level or controller's input signal have been determined in such a way as to limit the plasma drug concentration which also limits the toxic side effects. In addition, the particle swarm optimization (PSO) algorithm is employed to optimize the PID controller parameters. Simulation results show that the drug which is injected using this algorithm leads to a reduced number of cancer cells at the end of treatment while the normal cells population, despite the toxicity of the drug, remains almost in the acceptable range.

关键词： Cancer chemotherapy Drug scheduling feedback controller particle swarm optimization algorithm

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Performance optimization of DOA Estimation in Non-Uniform Linear Antenna Array by PSO algorithm

Performance Optimization of DOA Estimation in Non-Uniform Li...

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2nd International Conference on Mechanical Design and Power Engineering (ICMDPE 2013)

作者： Han, Huilian Wan, Liangtian Si, Weijian Beijing Aerosp Automat Control Inst Natl Key Lab Sci & Technol Aerosp Intelligence Co Beijing Peoples R China Harbin Engn Univ Informat & Commun Engn Coll Harbin Peoples R China

ISBN: (纸本)9783038350019

In order to estimate the coherent source, a modified multiple signal classification (MUSIC) algorithm is introduced. And a novel arrangement method for the non-uniform linear array by particle swarm optimization (PSO) algorithm is proposed. This method needs merely a signal source whose direction-of-arrival (DOA) has been exactly known. The proposed method has a simple processing and a strong stabilization. It could be applied to optimized arbitrary array configuration. The simulation verifies that the performance of DOA estimation is improved effectively, which has proved the validity of the proposed method.

关键词： DOA estimation non-uniform array particle swarm optimization algorithm

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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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Multi-Objective Optimal Control for Distribution Network Loop-Closing Operation Using PE-MOPSO algorithm 6

Multi-Objective Optimal Control for Distribution Network Loo...

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6th International Conference on Smart Grid and Electrical Automation (ICSGEA)

作者： Zhou, Shengchao Zhou, Yanping Wei, Yucheng Kang, Linchun Zhang, Xiaoli Yunnan Power Grid Co Ltd Kunming Power Supply Bur Kunming 650011 Yunnan Peoples R China

ISBN: (纸本)9781665432634

The loop-closing operation is usually used to transfer power supply without power interruption in the distribution network, but the excessive current induced by loop-closing will endanger the safety and stability of the distribution network. This paper presents a multi-objective optimal control method of loop-closing in distribution network. Firstly, the mathematical optimization model of loop-closing optimal regulation is established, which not only ensures the security of loop-closing operation, but also takes into account the stability and voltage quality of distribution network operation. Then, Pareto entropy-based multi-objective particle swarm optimization (PE-MOPSO) algorithm is proposed to solve the optimization problem. Finally, an actual example is simulated based on MATLAB platform. The simulation results show that the optimization model is correct, and PE-MOPSO algorithm has good diversity and convergence, and can give a reasonable control scheme.

关键词： Distribution network loop-closing optimal control Pareto entropy particle swarm optimization algorithm

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An Integrated Optical Method of PV-ESS-CHG V2G Station Based on PSO algorithm 6

An Integrated Optical Method of PV-ESS-CHG V2G Station Based...

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6th International Conference on Renewable Energy and Power Engineering (REPE)

作者： Zhu, Liang Zhang, Peizheng Deng, Yue Yang, Na Hou, Tong Zhang, Youcai Li, Xiaoyu Yu, Qingguang State Grid Beijing Chaoyang Elect Power Supply Co Beijing Peoples R China Tsinghua Univ Dept EE Beijing Peoples R China

ISBN: (纸本)9798350306194;9798350306187

In this paper, the basic structure of the optical storage and charging integrated charging station and the distribution control of energy in the system are discussed, and the capacity allocation model of the optical storage and charging system is established by considering the economic return of the charging station and the impact on the grid as the optimization objective, and the optimization solution is combined with the particle swarm optimization algorithm, which can support the planning and construction of the optical storage and charging integrated charging station.

关键词： Integrated Optical Method charging station capacity allocation particle swarm optimization algorithm

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A Route Self-Repair Method for Wireless Sensor Networks 24

A Route Self-Repair Method for Wireless Sensor Networks

引用

International Conference on Computer and Multimedia Technology (ICCMT)

作者： Guo, Chen Su, Mohan Cui, Fang China Mobile Grp Device Co Ltd Beijing 100053 Peoples R China

ISBN: (纸本)9798400718267

With the evolution of AI technology, several emerging applications (such as computational offload and image recognition) place higher demands on the quantity and quality of data transmitted by wireless sensor networks (WSNs). Existing routing algorithms typically choose the transmission path with fewer hops and shorter distance. For energy-constrained WSN, this will lead to rapid energy loss and loss of several nodes, which will shorten the network life cycle. In this paper, a WSN routing method based on the ant colony optimization algorithm and the particle swarm optimization algorithm is proposed to deal with the above-mentioned problems. By establishing the multidimensional pheromone model of node distance, hop number and energy, the nodes can dynamically adjust the routing according to the residual energy in the data transmission process, so as to ensure the balanced decrease of node energy and prolong the lifetime of the WSN network. In order to improve the convergence rate of ant colony algorithm and reduce the energy consumption caused by reconstruction, particle swarm optimization is applied;the fitness function of particle swarm optimization algorithm is improved to ensure that the path can quickly find the optimal solution of the path when multiple self-repairs or repair failures occur. The simulation shows that this method can improve the network lifetime by 33% by ensuring high transmission effectiveness.

关键词： Wireless sensor network Ant colony algorithm particle swarm optimization algorithm Network routing

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Short-Term Wind Generation Combined Forecast Considering Meteorological Similarity

Short-Term Wind Generation Combined Forecast Considering Met...

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IEEE IAS Conference on Industrial and Commercial Power System Asia (IEEE I and CPS Asia)

作者： Liu, Yating Yang, Ming Yu, Yixiao Ding, Tingting Si, Zhiyuan Li, Menglin Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Jinan Peoples R China

ISBN: (纸本)9781665434980

High-precision short-term wind generation prediction results are conducive to making a scientific generation plan and improving the wind power absorption capacity of the power grids. Based on the analysis of the relationship between the numerical weather prediction and wind power, this paper proposes a short-term wind generation combined forecast model considering meteorological similarity to improve the prediction accuracy of short-term wind power. In this method, the meteorological similarity day model, the extreme gradient boosting algorithm and the back propagation neural network algorithm are selected for achieving the short-term wind power prediction. Then, the particle swarm optimization algorithm is applied to determine the weight of each single forecasting model. Finally, the prediction results are obtained through the combination of the single model prediction results. With the realistic wind power data collected from a wind farm in Xinjiang province, the short-term wind forecasting task is achieved by the proposed method. The simulation results illustrate that the combined model proposed in this paper can effectively improve the forecasting performance of the benchmark models.

关键词： BP neural network algorithm Gaussian transform particle swarm optimization algorithm XGBoost short-term wind power combined forecast

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