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

共 1417 条记录，以下是161-170 订阅

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Band selection for hyperspectral image classification based on improved particle swarm optimization algorithm

Band selection for hyperspectral image classification based ...

引用

4th International Conference on Advances in Materials and Manufacturing (ICAMMP 2013)

作者： Li, Chenming Wang, Yan Gao, Hongmin Zhang, Lili Hohai Univ Coll Comp & Informat Engn Nanjing 211100 Jiangsu Peoples R China

ISBN: (纸本)9783038350156

Hyperspectral images have been widely used in earth observation. However, there are some problems such as huge amount of data and high correlation between bands. An application of particle swarm optimization algorithm based on B distance was proposed to band selection of hyperspectral images. First of all, bands are grouping by the correlation coefficient of the band and adjacent bands. B distance was used as separability criterion between classes and the fitness function comes into being. Finally, the classification results illustrate that the total classification accuracy of the proposed method is higher than the traditional method.

关键词： hyperspectral image B distance particle swarm optimization algorithm band selection

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Convergence Analysis of Self-adaptive Immune particle swarm optimization algorithm 15th

Convergence Analysis of Self-adaptive Immune Particle Swarm ...

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15th International Symposium on Neural Networks (ISNN)

作者： Jiang, Jingqing Song, Chuyi Ping, Huan Zhang, Chenggang Inner Mongolia Univ Nationalities Coll Comp Sci & Technol Tongliao 028043 Peoples R China Inner Mongolia Univ Nationalities Coll Math Tongliao 028043 Peoples R China

ISBN: (纸本)9783319925370;9783319925363

The self-adaptive immune particle swarm optimization (SAIPSO) algorithm is a hybrid algorithm based on immune algorithm and particle swarm optimization algorithm. SAIPSO algorithm has been implemented and achieved better result compared with the classical particle swarm optimization algorithm. However, the theoretical support of the algorithm is equally important as the implementation of the algorithm. Therefore, this paper mainly uses the convergence theorem of random search algorithm and the mathematical induction to prove the convergence of SAIPSO algorithm, which will help the improvement and application of the algorithm in the future.

关键词： Immune algorithm particle swarm optimization algorithm Self-adaptation Convergence

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A Reactive Power optimization Method for AC Metro Power Supply System Based on particle swarm optimization algorithm 19

A Reactive Power Optimization Method for AC Metro Power Supp...

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20th IEEE Vehicle Power and Propulsion Conference (IEEE VPPC)

作者： Feng, Ding Zhou, Haiqi Lin, Sheng Southwest Jiaotong Univ Sch Elect Engn Chengdu Peoples R China

ISBN: (纸本)9798350344455

Aiming at the problem of reactive power imbalance in AC metro, the equivalent circuit model is established by investigating the topology AC metro power supply system, and the change of reactive power in the whole day of the system is analyzed in combination with the actual operation data of the metro. The reactive power evaluation indices are proposed to evaluate the influence of reactive power on the system. To solve the problem that the power factor of the system at night does not meet the requirements and there is no available device in the line for reactive power optimization, the particle swarm optimization (PSO) is used to obtain the optimal configuration of reactive power compensation devices. The results show that the balance and optimization of reactive power can be achieved through reasonable configuration of compensation devices.

关键词： AC metro power supply system reactive power optimization particle swarm optimization algorithm

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Parameter optimization of ADRC for spacecraft attitude maneuver based on particle swarm optimization algorithm 6

Parameter optimization of ADRC for spacecraft attitude maneu...

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6th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Wang, Ping Wang, Hua Bai, Guoyu Su, Lin Co Postgrad Management Acad Equipment Beijing Peoples R China Acad Equipment Dept Space Equipment Beijing Peoples R China Natl Def Intellectual Property Bur Beijing Peoples R China

ISBN: (纸本)9781479949557

In this paper, parameter of ADRC for spacecraft attitude maneuvering is optimizated. Nonlinear dynamics model of spacecraft attitude describes attitude motion. particle swarm optimization algorithm is used for parameter optimization of ADRC. The controller index which describes attitude adjustment capacity of three axes is designed. The influence of controller parameter is quantifiable on the control performance. The selection of parameter based on traditonal experience is avoided. Simulation results show that: the particle swarm optimization algorithm for system updates through the position and velocity. The system can quickly converge to the global optimal solution, and the parameter of ADRC is optimized.

关键词： ADRC particle swarm optimization algorithm Parameter optimization

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A modified particle swarm optimization algorithm for support vector machine training

A modified particle swarm optimization algorithm for support...

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6th World Congress on Intelligent Control and Automation

作者： Yuan, Hejin Zhang, Yanning Zhang, Dengfu Yang, Gen Northwestern Polytech Univ Sch Comp Sci Xian 710072 Shaanxi Provinc Peoples R China

ISBN: (纸本)1424403316

A new modified particle swarm optimization algorithm for linear equation constrained optimization problem was put forward. And the method using this algorithm to train support vector machine was given. In the new algorithm, the particle studies not only from itself and the best one but also from other particles in the population with certain probability. This strengthened learning behavior can make the particle to search the whole solution space better. In addition, the mutation for the particle is considered in the new algorithm. The mutation operation is executed when the particle swarm becomes stagnated, which is decided by calculating the population diversity with the formula presented in this paper. For the specific constraints of support vector machine, a new method to initialize the particles in the feasible solution space was provided. The experiments on synthetic and sonar dataset classification show that our algorithm is feasible and robust for support vector machine training.

关键词： support vector machine particle swarm optimization algorithm mutation

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An effective particle swarm optimization algorithm for flexible job-shop scheduling problem

An effective particle swarm optimization algorithm for flexi...

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5th International Conference on Industrial Engineering and Systems Management (IEEE IESM)

作者： Nouiri, Maroua Jemai, Abderezak Ammari, Ahmed Chiheb Bekrar, Abdelghani Niar, Smail Carthage Univ INSAT Inst MMA Lab Tunis Tunisia King Abdulaziz Univ Fac Engn Jeddah 21413 Saudi Arabia Univ Lille Nord France F-59000 Lille France UVHC TEMPO Lab PSI Team F-59313 Valenciennes France UVHC LAMIH Lab DIM Team F-59313 Valenciennes France

ISBN: (纸本)9782960053241

Flexible job-shop scheduling problem (FJSP) is very important in many research fields such as production management and combinatorial optimization. The FJSP problems cover two difficulties namely machine assignment problem and operation sequencing problem. In this paper, we apply particle swarm optimization (PSO) algorithm to solve this FJSP problem aiming to minimize the maximum completion time criterion. Various benchmark data taken from literature, varying from Partiel FJSP and Total FJSP, are tested. Computational results proved that the PSO developed is enough effective and efficient to solve flexible job-shop scheduling problem.

关键词： flexible job-shop scheduling problem makespan particle swarm optimization algorithm routing and scheduling

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Center particle swarm optimization algorithm 3

Center Particle Swarm Optimization Algorithm

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IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Yang Xiaojing Jiao Qingju Liu Xinke Anyang Normal Univ Coll Comp & Informat Engn Anyang 455000 Peoples R China

ISBN: (纸本)9781538662434

The linear decreasing weight particle swarm optimization algorithm (IDWPSO) is mentioned in the concept of a center particle, and then puts forward center particle swarm optimization algorithm (PSO). The linear decreasing weight particle swarm optimization algorithm, unlike other general center particle, particle velocity center is not clear, and is always placed in the center of the particle swarm. In addition, the neural network training algorithm compared to particle swarm optimization algorithm and the linear decreasing weight particle swarm optimization algorithm, results show that: the performance is better than the linear optimization center particle swarm decreasing weight PSO algorithm. algorithm.

关键词： particle swarm optimization algorithm neural network evolutionary computation

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Logistics Distribution Location Based on particle swarm optimization algorithm

Logistics Distribution Location Based on Particle Swarm Opti...

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World Automation Congress (WAC)

作者： Chen, Xichun Wang, Junli Lanzhou Jiaotong Univ Sch Traff & Transportat Engn Lanzhou 730070 Peoples R China Gansu Prov Supervis & Inspect Ctr Product Qual Lanzhou Peoples R China

ISBN: (纸本)9781467344975

The aim of logistics distribution center location is to improve the logistics service quality and reduce the transportation cost of goods. The problems are related to both the classical location problem and the vehicle routing problem. This paper proposes logistics distribution center location based on particle swarm optimization algorithm. It models the logistics distribution center location problem considering the user demand and operating cost, then it uses modified particle swarm optimization algorithm to give solution. Experimental result shows that the proposed model and algorithm is effective.

关键词： distribution center location particle swarm optimization algorithm optional point

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optimization of Cable Force for Hybrid Girder Cable-Stayed Bridge Based on particle swarm optimization algorithm 8th

Optimization of Cable Force for Hybrid Girder Cable-Stayed B...

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8th International Conference on Civil Architecture and Structural Engineering (ICCASE)

作者： Xiao, Wenhao Changsha Univ Sci & Technol Sch Civil Engn Changsha Peoples R China

ISBN: (纸本)9783031761010;9783031761027

In order to make the hybrid girder cable-stayed bridge force reasonable, in line with the reasonable bridge formation state, the adaptive particle swarm optimization algorithm is used to optimize the cable-stayed bridge of hybrid girder cable-stayed bridge, the adaptive particle swarm optimization algorithm is written by using MATLAB software, and the finite element model is established by using Midas software for the analysis and calculation, and the objective function is constructed by using the influence matrix, and the value of the fitness is calculated, and the adaptive particle swarm optimization algorithm is used to find the global optimal solution. The results show that the vertical displacement of the main beam and the lateral displacement of the main tower are obviously reduced after optimization, and the bending moment of the main tower is obviously improved.

关键词： particle swarm optimization algorithm Hybrid girder cable-stayed bridge Cable-stayed force optimization Influence matrix

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Economic Dispatch Considering Ancillary Service Based on Revised particle swarm optimization algorithm

Economic Dispatch Considering Ancillary Service Based on Rev...

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6th International Conference on Intelligent Computing

作者： Ma, Xin Liu, Yong North China Univ Water Conservancy & Elect Power Sch Management & Econ Zhengzhou 450011 Henan Province Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9783642149214

A revised strategy particle swarm optimization algorithm is proposed to solve the economic dispatch problems in power systems Many constraints such as ramp rate limits and prohibited zones are taken into account and the loss is also calculated On the basis of strategy particle swarm optimization algorithm a new revised strategy is provided to handle the constraints and make sure the particles to satisfy the constraints The strategy can guarantee the particles to search in or around the feasible solutions area combined with penalty functions The accuracy and speed of the algorithm are improved for the particles will rarely search in the infeasible solutions area and the results also show that the new algorithm has a fast speed high accuracy and good convergence

关键词： Electricity market economic dispatch ancillary service particle swarm optimization algorithm revise strategy

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