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19th International Euromicro Conference on Parallel, Distributed and Network-Based Processing (PDP)

作者： Cardenas-Montes, Miguel Vega-Rodriguez, Miguel A. Jose Rodriguez-Vazquez, Juan Gomez-Iglesias, Antonio CIEMAT Dept Fundamental Reseach Madrid Spain Univ Extremadura ARCO Res Grp Caceres Spain CIEMAT Natl Fus Lab Madrid Spain

ISBN: (纸本)9780769543284

This paper focuses on solving large size optimization problems using GPGPU. Evolutionary algorithms for solving these optimization problems suffer from the curse of dimensionality, which implies that their performance deteriorates as quickly as the dimensionality of the search space increases. This difficulty makes very challenging the performance studies for very high dimensional problems. Furthermore, these studies deal with a limited time-budget. The availability of low cost powerful parallel graphics cards has stimulated the implementation of diverse algorithms on Graphics Processing Units (GPU). In this paper, the design of a GPGPU-based Parallel particle swarm algorithm, to tackle this type of problem maintaining a limited execution time budget, is described. This implementation profits of an efficient mapping of the data elements (swarm of very high dimensional particles) to the parallel processing elements of the GPU. In this problem, the fitness evaluation is the most CPU-costly routine, and therefore the main candidate to be implemented on GPU. As main conclusion, the speed-up curve versus the increase in dimensionality is shown. This curve indicates an asymptotic limit stemmed from the data-parallel mapping.

关键词： GPGPU Parallelism Performance Analysis particle swarm algorithm

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Equivalent Circuit Output Model for Runoff Hydropower Stations Based on Improved particle swarm algorithm 5

Equivalent Circuit Output Model for Runoff Hydropower Statio...

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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Jin, Xuan Wang, Shaorong Song, Zhaoxia Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan Hubei Peoples R China China Power Engn Consulting Grp Cent Southern China Elect Power Design Inst Co Lt Wuhan Hubei Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Hydropower plant output model is one of the key tools to promote the sustainable development of renewable energy and power systems which is of key importance in energy management and planning. Runoff hydropower stations with poor regulating ability have random and fluctuating output, so the realization of hydropower station output prediction is beneficial for adjusting the balance of supply and demand in the power system and formulating reasonable power generation plans and water resource management strategies. Based on the comprehensive consideration of the factors affecting the output of small hydropower, this paper focuses on the general model of small hydropower generation flow under the coupling of multiple factors. Combined with power system characteristics, a small hydropower output prediction model based on nonlinear circuit mapping is proposed. A parameter solving method based on improved particle swarm algorithm is proposed for the model, and finally the simulation is carried out through the actual data of a city, and the simulation results verify the feasibility, validity and advancement of the model. Output prediction modelling of hydropower plants can improve the resilience of the power system, reduce operating costs and ensure the stability of the power supply.

关键词： power prediction runoff hydropower station nonlinear circuit particle swarm algorithm

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A hybrid genetic and particle swarm algorithm for service composition

A hybrid genetic and particle swarm algorithm for service co...

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6th International Conference on Advanced Language Processing and Web Information Technology

作者： Liu, Jian Li, Jun'e Liu, Kaipei Wei, Wen Wuhan Univ Sch Elect Engn Wuhan 430072 Peoples R China Wuhan Univ Ctr Comp Wuhan 430072 Peoples R China Wuhan Municipal Meter Elect Polytech Sch Wuhan Peoples R China

ISBN: (纸本)9780769529301

Web Service Composition (WSC) has become a hotspot in recent research. Current solutions focus on ontology information representation and ontology based web service matching, which lacks flexibility. From simulation of human cognision, this paper proposed a hybrid Genetic particle swarm algorithm (GPSA) to solve the problem of WSC, which is a Multi-Objective Problem (MOP). Genetic algorithm (GA) is used to search throughout the problem space, and particle swarm Optimization (PSO) is used to enhance local search ability. PSO can reduce the calculation cost by trimming useless braches. Feedback information is used to decide how to balance GA and PSO, which means how to balance global and local optimization. Experiments show that GPSA can solve WSC Problem (WSCP) and balance between global and local optimization.

关键词： web service composition particle swarm algorithm genetic algorithm multi-objective problem

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The application of the projection pursuit model of the particle swarm algorithm on the investment decision

The application of the projection pursuit model of the parti...

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International Conference on Advanced Materials and Computer Science

作者： Deng Li Luo Fangqiong Liuzhou Teachers Coll Dept Finance & Econ Liuzhou 545004 Guangxi Peoples R China Liuzhou Teachers Coll Dept Math & Comp Sci Liuzhou 545004 Guangxi Peoples R China

ISBN: (纸本)9783037850978

In order to make a good decision in the projection investment, and the application of the projection pursuit model of particle swarm algorithm on the investment decision was studied in depth. Firstly, the development of the projection pursuit model of particle swarm algorithm was introduced, and then the brief introduction of PP and the step of constructing the PPC model were introduced. The basic theory and calculating procession of particle swarm algorithm was analyzed. And then the projection investment decision model was established. Finally a case study was carried out and the results showed that this method was simple and effective.

关键词： projection pursuit model particle swarm algorithm investment decision

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A New Parameters Identification Method for Novel Synchronous Condenser Based on particle swarm algorithm and Wavelet Transform 21

A New Parameters Identification Method for Novel Synchronous...

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21st International Conference on Electrical Machines and Systems (ICEMS)

作者： Ma, Yiming Zhou, Libing Wang, Jin Zhao, Junchen Shentu, Leixuan Xiao, Yang Huazhong Univ Sci & Technol Sch Elect & Elect Engn State Key Lab Adv Electromagnet Engn & Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9788986510201

In order to improve the safe and stable operation level of ultra-high voltage direct current power transmission projects, the novel synchronous condenser is adopted as a new dynamic reactive power compensation device. The accurate parameters identification is crucial to the analysis of the novel synchronous condenser's operating characteristics. This paper proposes a new parameters identification method for the novel synchronous condenser, which is based on DC step voltage test. particle swarm algorithm and wavelet transform are used in this process. A detailed 2D FEA model of novel synchronous condenser is established. Then the DC step voltage test is carried out and wavelet transform is used to preprocess the obtained current response. Finally, the accurate performance parameters are identified using particle swarm optimization algorithm.

关键词： Parameters Identification DC step voltage test novel synchronous condenser particle swarm algorithm wavelet transform

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Clustering algorithm based on improved particle swarm algorithm

Clustering algorithm based on improved particle swarm algori...

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International Forum on Materials Science and Industrial Technology (IFMSIT 2013)

作者： Yang, Jinhui Cao, Xi Baoding Vocat & Tech Coll Modern Educ Technol Dept Baoding City Peoples R China Baoding Vocat & Tech Coll Comp & Informat Engn Dept Baoding City Peoples R China

ISBN: (纸本)9783037858417

K-means algorithm is a traditional cluster analysis method, has the characteristics of simple ideas and algorithms, and thus become one of the commonly used methods of cluster analysis. However, the K-means algorithm classification results are too dependent on the choice of the initial cluster centers for some initial value, the algorithm may converge in general suboptimal solutions. Analysis of the K-means algorithm and particle swarm optimization based on a clustering algorithm based on improved particle swarm algorithm. The algorithm local search ability of the K-means algorithm and the global search ability of particle swarm optimization, local search ability to improve the K-means algorithm to accelerate the convergence speed effectively prevent the occurrence of the phenomenon of precocious puberty. The experiments show that the clustering algorithm has better convergence effect.

关键词： cluster analysis K-means algorithm particle swarm algorithm convergence rate globally optimal

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Research on UAV Task Assignment Based on Hybrid particle swarm algorithm 4

Research on UAV Task Assignment Based on Hybrid Particle Swa...

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4th International Conference on Intelligent Autonomous Systems (ICoIAS)

作者： Li Yeding Dai Jiyang Nanchang Hangkong Univ Sch Informat Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781665441957

Aiming at the task planning of UAVs at the tactical level, this paper uses particle swarm algorithm as the basis and uses A* algorithm to complete the voyage estimation between tasks. In the task allocation stage, the initial planning of the coordinated route between UAVs is completed simultaneously to realize the tight coupling of task allocation and route planning. When considering more obstacle threats, or even sudden threats, the algorithm can effectively complete the coordinated task assignment of multiple drones to more targets. And the accuracy is greatly improved, the total execution cost is small, and the load between machines is reasonable.

关键词： UAVs Task planning Task Assignment Route planning particle swarm algorithm Multi-machine coordination

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Forecasting Method of Crude Oil Output Based on Optimization of LSSVM by particle swarm algorithm 4

Forecasting Method of Crude Oil Output Based on Optimization...

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4th International Conference on Information Science and Control Engineering (ICISCE)

作者： Hu, Hongtao Hu, Xin Guan, Xin Xian Shiyou Univ Sch Comp Sci Xian Shaanxi Peoples R China Res Inst Petr Explorat & Dev LangFang Langfang Hebei Peoples R China

ISBN: (纸本)9781538630136

The accurate prediction of crude oil output plays an important role in the development of oilfield planning. This paper proposes a least squares support vector machine model based on the optimization of particle swarm algorithm (PSO-LSSVM) to predict the crude oil output. Each pair of penalty factor and kernel function parameter was taken as a particle, which follows the optimal particle in the current solution space and adjusts the search direction and speed accordingly. The optimal penalty factor and kernel function parameter were determined by fitness function, and then the optimal LSSVM model was obtained. In this paper, we studied the relationship between crude oil production and its influencing factors by using this model. The experimental results showed that it has fast convergence speed and high prediction accuracy. This study might contribute to the development of the oilfield planning. Moreover, this model will provide a useful reference for the prediction of other dynamic production indexes in oilfield development.

关键词： Crude oil output forecasting particle swarm algorithm LSSVM parameters tuning

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Control strategy of maglev vehicles based on particle swarm algorithm

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Journal of Modern Transportation 2014年第1期22卷 30-36页

作者： Hui Wang Gang Shen Jinsong Zhou Institute of Railway and Urban Mass Transit Tongji University

Taking a single magnet levitation system as theobject, a nonlinear numerical model of the vehicle–guidewaycoupling system was established to study the levitationcontrol strategies. According to the similarity in dynamics,the single magnet-guideway coupling system was simplifiedinto a magnet-suspended track system, and the correspondinghardware-in-loop test rig was set up usingdSPACE. A full-state-feedback controller was developedusing the levitation gap signal and the current signal, andcontroller parameters were optimized by particle swarmalgorithm. The results from the simulation and the test rigshow that, the proposed control method can keep the systemstable by calculating the controller output with the fullstateinformation of the coupling system, Step responsesfrom the test rig show that the controller can stabilize thesystem within 0.15 s with a 2 % overshot, and performswell even in the condition of violent external *** the linear quadratic optimal method, the particleswarm algorithm carries out the optimization with thenonlinear controlled object included, and its optimizedresults make the system responses much better.

关键词： Maglev control Vehicle–guideway couplingvibration particle swarm algorithm Full-state feedback

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The research of particle swarm algorithm based on heuristic rules for the layout of airplane equipment cabin

The research of particle swarm algorithm based on heuristic ...

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2nd International Conference on Mechatronics and Applied Mechanics (ICMAM2012)2nd International Conference on Mechatronics and Applied Mechanics (ICMAM2012)

作者： Han, Xiaojian Ding, Xiangfang Xiao, Chun Beijing Univ Aeronaut & Astronaut Sch Mech Engn & Automat Beijing 100191 Peoples R China

ISBN: (纸本)9783037856512

How to get the most optimal solution of equipment layout in the aircraft cabin of the limited space is a completely NP problem. The problem is abstracted as three dimensions (3D) layout problem. A co-evolutionary particle swarm optimization with heuristic rules is presented. The cabin is decomposed into several small-scale layout problems. The co-evolutionary framework is adopted, and particle swarm optimization (PSO) and heuristic roles for layout are integrated to solve this problem. Finally, an example is used to verify the feasibility and effectiveness of the algorithm.

关键词： airplane equipment cabin particle swarm algorithm layout optimization 3D layout heuristic rules

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