The optimization of wind turbine blades can increase the generator power and annual output of electricity. Illustrated by the case of 2MW wind turbine, optimizing the blade chord and twist angle by POS algorithm. Mode...
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ISBN:
(纸本)9783037854471
The optimization of wind turbine blades can increase the generator power and annual output of electricity. Illustrated by the case of 2MW wind turbine, optimizing the blade chord and twist angle by POS algorithm. Modeling by BLADED, analysis the change of lift and drag coefficients, the power coefficient, maximum power of wind turbine, minimum power of wind turbine, wind turbine generating capacity before and after optimization. The results show: the aerodynamic efficiency of optimized blade increased by 4.833 than that before optimization. In the wind speed of 12 m/s (that is lower and normal speed), the average power coefficient is improved by 0.05. The minimum power of the wind turbine increased by 4% -15%.The maximum power of the wind turbine increased by 3% -9%. And the annual production of power increased by 0.25%.
Fuzzy c-means algorithm (FCM) is one of the most widely used clustering methods for modern medical image segmentation applications. However the conventional FCM algorithm has certain possibilities of converging to a l...
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ISBN:
(纸本)9783642289415;9783642289422
Fuzzy c-means algorithm (FCM) is one of the most widely used clustering methods for modern medical image segmentation applications. However the conventional FCM algorithm has certain possibilities of converging to a local minimum of the objective function, thus lead to undesired segmentation results. To address this issue, an improved FCM which is based on clustering centroids updates with the use of particleswarmoptimization (PSO) is proposed in this paper. This algorithm is designed to support multidimensional feature data and be accessible through parallel computation. The experimental results suggest that, compared to the conventional FCM algorithm, the proposed algorithm leads to higher chances of global optimum clustering and is less computationally intensive when large clustering number is needed.
In the area of association rule mining, most previous research had focused on improving computational efficiency. However, determination of the threshold values of support and confidence, which seriously affect the qu...
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In the area of association rule mining, most previous research had focused on improving computational efficiency. However, determination of the threshold values of support and confidence, which seriously affect the quality of association rule mining, is still under investigation. Thus, this study intends to propose a novel algorithm for association rule mining in order to improve computational efficiency as well as to automatically determine suitable threshold values. The particle swarm optimization algorithm first searches for the optimum fitness value of each particle and then finds corresponding support and confidence as minimal threshold values after the data are transformed into binary values. The proposed method is verified by applying the FoodMart2000 database of Microsoft SQL Server 2000 and compared with a genetic algorithm. The results indicate that the particle swarm optimization algorithm really can suggest suitable threshold values and obtain quality rules. In addition, a real-world stock market database is employed to mine association rules to measure investment behavior and stock category purchasing. The computational results are also very promising. (C) 2009 Elsevier B.V. All rights reserved.
Electricity demand forecasting plays an important role in electric power systems planning. In this paper, nonlinear time series modeling technique is applied to analyze electricity demand. Firstly, the phase space, wh...
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Electricity demand forecasting plays an important role in electric power systems planning. In this paper, nonlinear time series modeling technique is applied to analyze electricity demand. Firstly, the phase space, which describes the evolution of the behavior of a nonlinear system, is reconstructed using the delay embedding theorem. Secondly, the largest Lyapunov exponent forecasting method (LLEF) is employed to make a prediction of the chaotic time series. In order to overcome the limitation of LLEF, a weighted largest Lyapunov exponent forecasting method (WLLEF) is proposed to improve the prediction accuracy. The particle swarm optimization algorithm (PSO) is used to determine the optimal weight parameters of WLLEF. The trend adjustment technique is used to take into account the seasonal effects in the data set for improving the forecasting precision of WLLEF. A simulation is performed using a data set that was collected from the grid of New South Wales, Australia during May 14-18, 2007. The results show that chaotic characteristics obviously exist in electricity demand series and the proposed prediction model can effectively predict the electricity demand. The mean absolute relative error of the new prediction model is 2.48%, which is lower than the forecasting errors of existing methods. (C) 2011 Elsevier Ltd. All rights reserved.
An improved particle swarm optimization algorithm-niche particle swarm optimization algorithm (NichePSO) is proposed in this paper. The new algorithm can ensure that the particles have enough energy to move during opt...
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An improved particle swarm optimization algorithm-niche particle swarm optimization algorithm (NichePSO) is proposed in this paper. The new algorithm can ensure that the particles have enough energy to move during optimizing. Besides, the particle with poor fitness will be restarted in search space if its position is very close to the position of another particle with better fitness. Both NichePSO and PSO are used to solve four test functions' optimization problems. Results show that NichePSO has better optimization performance than PSO.
To improve performance of particleswarmoptimization (PSO) algorithm and avoid trapping to local minima, a multi-population parallel particleswarmoptimization (DPPSO) algorithm is proposed. In the algorithm, sub po...
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ISBN:
(纸本)9787811240559
To improve performance of particleswarmoptimization (PSO) algorithm and avoid trapping to local minima, a multi-population parallel particleswarmoptimization (DPPSO) algorithm is proposed. In the algorithm, sub populations are divided into exploration and exploitation types. The global version PSO is used in the exploration population to enhance ability of exploring the best individual, and the local version PSO is used in the exploitation population to enhance ability of local search and find the best global result in the local range. Simultaneously, keep communication with sub populations in running. The experimental results show that the restraining premature convergence is enhanced for maintaining the individual diversity.
The combination of the synthetic minority over-sampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data. In order to enhance the s...
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ISBN:
(纸本)9781424496365
The combination of the synthetic minority over-sampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier structure and the parameters of RBF kernels are determined using a particle swarm optimization algorithm based on the criterion of minimizing the leave-one-out misclassification rate. The experimental results on both simulated and real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.
This paper presents an algorithm based on OBB bounding box and particleswarm hybrid collision detection algorithm. algorithm use hierarchical bounding boxes OBB Rapid exclude some objects do not intersect, only the n...
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ISBN:
(纸本)9783642145322
This paper presents an algorithm based on OBB bounding box and particleswarm hybrid collision detection algorithm. algorithm use hierarchical bounding boxes OBB Rapid exclude some objects do not intersect, only the nodes in the collision of the use of particleswarmoptimization in the searching. To play a level bounding box algorithm and improved particleswarm-based collision detection algorithm Random their own advantages. Finally, Experimental results demonstrate the effectiveness of hybrid collision detection algorithm.
In this paper we present partitioning for simultaneous cut size and circuit delay minimization. Due to the random search, of simulated annealing algorithms, the solution of a circuit partitioning problem is global opt...
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In this paper we present partitioning for simultaneous cut size and circuit delay minimization. Due to the random search, of simulated annealing algorithms, the solution of a circuit partitioning problem is global optimum. [1] The circuit is divided into partitions and number of interconnections between them is minimized. [2] PSO employs a collaborative population-based search, which is inspired by the social behavior of bird flocking. It combines local search (by self experience) and global search (by neighboring experience), possessing high search efficiency. SA employs certain probability to avoid becoming trapped in a local optimum and the search process can be controlled by the cooling schedule. Experimental result shows that the developed hybrid PSO and SA algorithm can consistently produce the better result than the other algorithms.
To improve the scheduling optimization of mass customization collaborative logistics, a scheduling solution developed based on the novel particle swarm optimization algorithm was proposed. A mathematic model based on ...
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To improve the scheduling optimization of mass customization collaborative logistics, a scheduling solution developed based on the novel particle swarm optimization algorithm was proposed. A mathematic model based on scheduling strategy of mass customization logistics was designed. The novel dynamic particle swarm optimization algorithm framework was given. And simulation experiments were done to validate algorithm. Experiment results show that the proposed algorithm effectively improves the scheduling optimization of mass customization collaborative logistics, which has direct applications for Logistics scheduling
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