Vertical particle swarm optimization algorithm (VPSO) is proposed in this paper. The new algorithm assumes that the particles tend to fly towards two directions. One is flying toward the global best particle. The othe...
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ISBN:
(纸本)9781424409723
Vertical particle swarm optimization algorithm (VPSO) is proposed in this paper. The new algorithm assumes that the particles tend to fly towards two directions. One is flying toward the global best particle. The other is flying toward the vertical direction. And there is a random value produced in every iteration step to measure the probability of flying into two directions. Both VPSO and particle swarm optimization algorithm (PSO) are used to train neural network (NN) and applied in soft-sensor of acrylonitrile yield. Finally, simulation results show that the method proposed by this paper is feasible and effective in soft-sensor of acrylonitrile yield.
The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. In this work, for determining the competitive learning model, the particleswarmoptimization (PSO...
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ISBN:
(纸本)9781424410200
The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. In this work, for determining the competitive learning model, the particleswarmoptimization (PSO) technique is used as a training algorithm to adjust the weights of the artificial neural networks (ANNs) model to predict hourly loads. The feature of PSO is to fly potential solutions through hyperspace, accelerating toward better solutions. Thus the training phase should result in obtaining the weights configuration associated with the minimum output error. The historical load and weather information were trained and tested over a period of one season through two years. Generalized error estimation is done by using the reverse part of the data as a "test" set. The results were compared with conventional back-propagation algorithm and yielded encouraging results.
Threshold extraction is the fundamental step in multi-threshold image segmentation. This paper has introduced particle swarm optimization algorithm (PSO) for threshold extraction. But when dealing with the peaky high ...
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ISBN:
(纸本)9781424409723
Threshold extraction is the fundamental step in multi-threshold image segmentation. This paper has introduced particle swarm optimization algorithm (PSO) for threshold extraction. But when dealing with the peaky high dimension function of maximum entropy for multi-threshold image segmentation, the conventional PSO is apt to be trapped in local optima called premature. This can cause image segmentation failure. This paper proposes a modified particleswarmoptimization method (MPSO), which improves convergence speed and search capacity and avoid the premature phenomena when used in threshold extraction. Simulation results show that the MPSO has better performance and quicker speed. The experimental results also show that with the modified PSO as a threshold extraction method, the image is segmented fairly well and the segmentation speed improves greatly.
This paper describes a kind of robust texture feature invariant to rotation and scale changes, which is the texture energy associated with a mask generated by particle swarm optimization algorithms. The detail procedu...
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ISBN:
(纸本)9780819469533
This paper describes a kind of robust texture feature invariant to rotation and scale changes, which is the texture energy associated with a mask generated by particle swarm optimization algorithms. The detail procedure and algorithm to generate the mask is discussed in the paper. Furthermore, feature extraction experiments on aerial images are done. Experimental results indicate that the robust feature is effective and PSO-based algorithm is a viable approach for the "tuned" mask training problem.
For the problem that manual adjustment of the parameters of controller in sensorless control system costs too much time, manpower and always can not get a good result, a new method based on improved particleswarm opt...
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ISBN:
(纸本)9783037852958
For the problem that manual adjustment of the parameters of controller in sensorless control system costs too much time, manpower and always can not get a good result, a new method based on improved particle swarm optimization algorithm is proposed to optimize the parameters. The improved algorithm is based on the standard particleswarmoptimization with the simulated annealing algorithm and chaotic search brought in. The speed of motor is estimated by the extend Kalman filter. The error between measured speed and estimated speed of the permanent magnet synchronous motor rotor is used as the fitness function in order that the parameters in the covariance matrix is *** result of simulation indicates that high estimation precision can be got and the motor represents steadily with few of ripple of the actual *** this method, the time of adjustment is reduced and manpower is saved. In addition, the validity of the method is proved in experiment with dSPACE.
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 (C) 2011 Published by Elsevier Ltd.
The unit commitment problem is to reduce the total generation cost as much as possible while satisfying future power demands. Therefore, optimization must be performed based on correct predictions of future demands. H...
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ISBN:
(纸本)9780769547633;9781467321389
The unit commitment problem is to reduce the total generation cost as much as possible while satisfying future power demands. Therefore, optimization must be performed based on correct predictions of future demands. However, various uncertain factors affect these loads making an exact forecasting unsuccessful. This study mitigates this difficulty by applying fuzzy set theory and the objective is to build a two-stage multi-objective fuzzy programming model. To define the supply reliability effectively, we propose a new concept of maximal blackout time based on the fuzzy credibility theory. In addition, an improved two-layer multi-objective particle swarm optimization algorithm is designed as the solution. Finally, the performance of this study is discussed in comparison with experimental results from several test systems.
The hybrid wind/PV/pumped-storage power system was the hybrid system which combined hybrid wind/PV system and pumped-storage power station. System optimization was very important in the system design process. particle...
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ISBN:
(纸本)9783037854143
The hybrid wind/PV/pumped-storage power system was the hybrid system which combined hybrid wind/PV system and pumped-storage power station. System optimization was very important in the system design process. particle swarm optimization algorithm was a stochastic global optimizationalgorithm with good convergence and high accuracy, so it was used to optimize the hybrid system in this paper. First, the system reliability model was established. Second, the particle swarm optimization algorithm was used to optimize the system model in Nanjing. Finally, The results were analyzed and discussed. The optimization results showed that the optimal design method of wind/PV/pumped-storage system based on particleswarmoptimization could take into account both the local optimization and the global optimization, which has good convergence high precision. The optimal system was that LPSP (loss of power supply probability) was zero.
This paper is concerned with the optimal placement of protection devices in a microgrid using particle swarm optimization algorithm. One of the main advantages of Distributed Generation (DG) scheme and microgrids in m...
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ISBN:
(纸本)9781467350723
This paper is concerned with the optimal placement of protection devices in a microgrid using particle swarm optimization algorithm. One of the main advantages of Distributed Generation (DG) scheme and microgrids in modern distribution systems is the reduction of number of outages and the associated damages caused by them. This task is accomplished by supplying a feeder from multiple sources. In order to prevent generator instability in DGs connected to utility, it is necessary to improve the protective schemes of traditional distribution systems and also to use proper relaying and setting for DGs. All of the downstream overcurrent (OC) relays of each DG are coordinated together and also should be coordinated with OC relay that is installed on the Point of Common Coupling (PCC) which is set at Critical Clearing Time (CCT) as a definite time, to have a desirable performance on each outage. In this paper, by the use of graph theory, various branches of a feeder are identified and the constraints for using particle swarm optimization algorithm to optimize the location of protective equipment are derived. In the proposed algorithm, the location, type and direction of relays are optimized simultaneously.
In this paper, according to the characteristics, dynamical behavior and the experimental data of the batch anaerobic culture, a parameter identification model was improved to describe the dynamical system for microorg...
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ISBN:
(纸本)9783037855027
In this paper, according to the characteristics, dynamical behavior and the experimental data of the batch anaerobic culture, a parameter identification model was improved to describe the dynamical system for microorganism in batch fermentation. And some relative characters were introduced. Finally, a PSO algorithm with the inertia weight was used to get the best optimal parameter of the identification model. The results show that the model reduces the errors between the experimental data and computational values, and they can simulate the process of batch fermentation better.
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