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

共 1417 条记录，以下是181-190 订阅

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Ship Heading Control Based on Improved particle swarm optimization algorithm 1st

Ship Heading Control Based on Improved Particle Swarm Optimi...

引用

World Conference on Intelligent and 3-D Technologies (WCI3DT)

作者： Liang, Hao Wang, Huaming Fan, Lingmei Wang, Xinshuai Chen, Xiaoyu Zhejiang Ocean Univ Sch Marine Engn Equipment Zhoushan Peoples R China Zhejiang Ocean Univ Sch Naval Architecture & Maritime Zhoushan Peoples R China

ISBN: (纸本)9789811971860;9789811971846;9789811971839

Ship course control is designed by means of ship response mathematical model, servo system of steering engine, wave interference model, and PID controller, and particle swarm optimization algorithm (PSO) is added to the course control to obtain the optimal PID parameter value. Considering that the number of PSO iterations and the objective function in the search process have a certain influence on the PID parameter value, this paper mainly studies changing the iteration number and objective function to compare the corresponding PID parameters and then obtains the optimal control effect according to different PID parameters corresponding to different control effects.

关键词： Response mathematical model particle swarm optimization algorithm Iteration times The objective function

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Partner selection of agricultural product supply chain based on discrete particle swarm optimization algorithm 4

Partner selection of agricultural product supply chain based...

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4th International Conference on Applied Mechanics, Materials and Manufacturing (ICAMMM)

作者： Chen Ailing Shandong Univ Finance & Econ Sch Management Sci & Engn Jinan 250014 Shandong Peoples R China

ISBN: (纸本)9783038352884

Partner selection decisions are an important component of production and supply chain management. However, partner selection is a typical multi-criteria decision-making problem and many factors affect partner selection;it is hard to select the most appropriate partners in real circumstances, especially in agricultural product supply chain. In this study, a discrete particle swarm optimization algorithm is proposed to solve the problem. A numerical example for partner selection of agricultural product supply chain is given to illustrate the application of the proposed method and shows the proposed method is feasible and effective.

关键词： Partner selection agricultural product supply chain particle swarm optimization algorithm

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A new Combined particle swarm optimization algorithm Based Golden Section Strategy

A new Combined Particle Swarm Optimization Algorithm Based G...

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International Conference on Advanced Design and Manufacturing Engineering (ADME 2011)

作者： Hui, Fan Shanghai Dianji Univ Sch Elect Engn Shanghai Peoples R China

ISBN: (纸本)9783037852132

Based the defects of global optimal model falling into local optimum easily and local model with slow convergence speed during traditional PSO algorithm solving a complex high-dimensional and multi-peak function, a two sub-swarms particle optimization algorithm is proposed. All particles are divided into two equivalent parts. One part particles adopts global evolution model, while the other part uses local evolution model. If the global optimal fitness of the whole population stagnates for some iteration, a golden rule is introduced into local evolution model. This strategy can substitute the partial perfect particles of local evolution for the equivalent worse particles of global evolution model. So, some particles with advantage are joined into the whole population to make the algorithm keep active all the time. Compared with classic PSO and PSO-GL(A dynamic global and local combined particle swarm optimization algorithm, PSO-GL), the results show that the proposed PSO in this paper can get more effective performance over the other two algorithm in the simulation experiment for four benchmark testing function.

关键词： particle swarm optimization algorithm golden rule diversity model optimization

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STEEL LAZY WAVE RISER optimization USING particle swarm optimization algorithm 42

STEEL LAZY WAVE RISER OPTIMIZATION USING PARTICLE SWARM OPTI...

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ASME 42nd International Conference on Ocean, Offshore and Arctic Engineering (OMAE)

作者： Lal, Mayank Yenduri, Anurag Sebastian, Abhilash TechnipFMC Houston TX 77044 USA TechnipFMC Chennai Tamil Nadu India

ISBN: (纸本)9780791886854

Steel lazy wave risers have become a preferred riser solution as offshore oil and gas developments are happening in deeper waters and harsher environments. Steel lazy wave risers work well with FPSOs even though FPSO motions are worse compared to other floating platforms such as Spars and Semisubmersibles. FPSOs offer a tremendous advantage compared to other floating platforms as they can be used in regions with inadequate pipeline infrastructure to transport oil and gas to shore. The targeted placement of buoyancy modules on steel lazy wave risers improves the strength and fatigue performance compared to steel catenary risers making them feasible for use with FPSO's. Also, buoyancy modules on steel lazy wave risers help reduce the top tension compared to steel catenary risers which gains significance in ultra-deepwater developments where top tensions for steel catenary risers make them an infeasible riser solution. Steel lazy wave riser configuration design is dependent on a few parameters such as hang-off angle, length to start of buoyancy section, length of buoyancy section, type of buoyancy, and buoyancy factor. Theoretically, there are infinite solutions for a steel lazy wave riser configuration. Hence, the design of steel lazy wave riser is an optimization problem in which the parameters that govern the design can be varied to get an optimized configuration. In this paper, the particle swarm algorithm which is a bio-inspired algorithm is used to find an optimal solution for steel lazy wave riser configuration. The solution space is searched for an optimal steel lazy wave riser configuration based on an objective function which is a function of strength response, fatigue response, and cost. Solutions with poor interference response are penalized in the algorithm and rejected as part of the solution space search process. Finding an optimal solution automatically will help reduce the overall cost of risers which is a significant part of any offshore development.

关键词： SLWR optimization particle swarm optimization algorithm

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Constrained Multi-objective particle swarm optimization algorithm

Constrained Multi-objective Particle Swarm Optimization Algo...

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8th International Conference on Intelligent Computing (ICIC)

作者： Gao, Yue-lin Qu, Min N Ethn Univ Inst Informat & Syst Sci Yinchuan Ningxia Peoples R China

ISBN: (纸本)9783642318368

A particle swarm optimization for solving constrained multi-objective optimization problem was proposed (CMPSO). In this paper, the main idea is the use of penalty function to handle the constraints. CMPSO employs particle swarm optimization algorithm and Pareto neighborhood crossover operation to generate new population. Numerical experiments are compared with NSGA-II and MOPSO on three benchmark problems. The numerical results show the effectiveness of the proposed CMPSO algorithm.

关键词： particle swarm optimization algorithm constrained multi-objective optimization Pareto neighborhood crossover operation penalty function method

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P2P Traffic Identification Using Support Vector Machine and Cuckoo Search algorithm Combined with particle swarm optimization algorithm 1

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3rd CCF Internet Conference of China (CCF ICoC)

作者： Ye, Zhiwei Wang, Mingwei Wang, Chunzhi Xu, Hui Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China

ISBN: (数字)9783662468265

ISBN: (纸本)9783662468265;9783662468258

As peer-to-peer (P2P) technology booms lots of problems arise such as rampant piracy, congestion, low quality etc. Thus, accurate identification of P2P traffic makes great sense for efficient network management. As one of the optimal classifiers, support vector machine (SVM) has been successfully used in P2P traffic identification. However, the performance of SVM is largely dependent on its parameters and the traditional tuning methods are inefficient. In the paper, a novel hybrid method to optimize parameters of SVM based on cuckoo search algorithm combined with particle swarm optimization algorithm is proposed. The first stage of the proposed approach is to tune the best parameters for SVM with training data. Subsequently, the SVM configured with the best parameters is employed to identify P2P traffic. In the end, we demonstrate the effectiveness of our approach on-campus traffic traces. Experimental results indicate that the proposed method outperforms SVM based on genetic algorithm, particle swarm optimization algorithm and cuckoo search algorithm.

关键词： P2P traffic identification Support vector machine Cuckoo search algorithm particle swarm optimization algorithm

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Grey Model Based particle swarm optimization algorithm for Anticorrosion Reliability Design of Underground Pipelines

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2nd International Conference on Advances in Product Development and Reliability (PDR)

作者： Liu, Qinming Dong, Ming Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Coll Econ & Management Shanghai 200052 Peoples R China

ISBN: (纸本)9780878492541

This paper explores the grey model based PSO (particle swarm optimization) algorithm for anti-cauterization reliability design of underground pipelines. First, depending on underground pipelines' corrosion status, failure modes such as leakage and breakage are studied. Then, a grey GM(1,1) model based PSO algorithm is employed to the reliability design of the pipelines. One important advantage of the proposed algorithm is that only fewer data is used for reliability design. Finally, applications are used to illustrate the effectiveness and efficiency of the proposed approach.

关键词： Underground pipeline Reliability design Grey theory particle swarm optimization algorithm

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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...

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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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Support Vector Regression and particle swarm optimization algorithm for Intelligent Electronic Circuit Fault Diagnosis

Support Vector Regression and Particle Swarm Optimization Al...

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2nd IEEE International Conference on Computer Science and Information Technology

作者： Tian, WenJie Tian, Yue Ai, Lan Liu, JiCheng Beijing Union Univ Automat Inst Beijing Peoples R China Univ Cape Economics & Business Finance Inst Beijing Peoples R China

ISBN: (纸本)9781424445189

In the analysis of electronic circuit fault diagnosis based on support vector regression (SVR), irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of prediction accuracy. In order to solve the problems mentioned above, this paper used rough sets as a preprocessor of SVR to select a subset of input variables and employed the particle swarm optimization algorithm (PSOA) to optimize the parameters of SVR. Additionally, the proposed PSOA-SVR model that can automatically determine the optimal parameters was tested on the prediction of electronic circuit fault. Then, we compared the proposed PSOA-SVR model with other artificial intelligence models of (BPN and fix-SVR). The experiment indicates that the proposed method is quite effective and ubiquitous.

关键词： fault diagnosis rough set particle swarm optimization algorithm support vector regression electronic circuit

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Evaluation of particle swarm optimization algorithm in Photovoltaic Applications 10

Evaluation of Particle Swarm Optimization Algorithm in Photo...

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10th International Conference on Intelligent Systems and Control (ISCO)

作者： Malarvizhi, E. Kamala, J. Sivasubramanian, A. St Josephs Coll Engn Dept ECE Madras Tamil Nadu India Anna Univ Dept ECE CEG Madras Tamil Nadu India VIT Univ Sch Elect Engn Madras Tamil Nadu India

ISBN: (纸本)9781467378079

Solar energy is the prime source of consumption for the world. It is the potential candidate for meeting the growing energy demand and solving environmental issues. To derive the maximum Power (MP) from the system, Maximum Power Point Tracking (MPPT) methods are implemented. It is highly essential to derive MP from the available solar energy. Over the years, numerous MPPT methods have been developed and presented in the literature. This paper discusses the evaluation of particle swarm optimization (PSO) algorithm in MPPT based solar power generation systems. It describes the different methodologies adopted to extract the maximum power from the solar array in photovoltaic (PV) power systems.

关键词： Photovoltaic power system Maximum Power Point Tracking particle swarm optimization algorithm

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