Distribution Network Reconfiguration (DNR) is required to identify the best topology network in order to fulfill the power demand with minimum power losses. This paper proposes a new method which is called as Rank Evo...
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ISBN:
(纸本)9781479938599
Distribution Network Reconfiguration (DNR) is required to identify the best topology network in order to fulfill the power demand with minimum power losses. This paper proposes a new method which is called as Rank evolutionary Particle Swarm Optimization (REPSO). The proposed method is a combination of the Particle Swarm Optimization (PSO) and the traditional evolutionary programming (EP) algorithm with a rejuvenation of the additional of ranking element. The main objective of this paper is to reduce the power losses while improving the convergence time. The proposed method will be implemented and the real power losses in the IEEE 33-bus test system will be investigated and analyzed accordingly. The results are compared to the conventional PSO and hybridization EPSO method and it is hoped to help the power system engineer in securing the network with the less power loss in the future.
The runoff series always presents complex chaos phenomenon with higher embedded dimensions because of the influence of many complicated ***,it is an effective method to combine phase space restructures theory with art...
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The runoff series always presents complex chaos phenomenon with higher embedded dimensions because of the influence of many complicated ***,it is an effective method to combine phase space restructures theory with artificial neural networks(ANN) model for runoff *** traditional methods that not consider the variations of the character of full Lyapunov exponential spectrum in restructure space,are proved of high precision to forecast time series with low-embedded ***,they are not so effective to forecast attractors with high-embedded *** paper proposes a new method,which improves the character of full Lyapunov exponential spectrum in a restructure space with high *** the mean time,an artificial neural networks model based on determinate mutation evolutionary programming(DMEP) learning algorithm is presented for chaotic runoff series *** introduces chaos mapping into the mutation operation of EP,which aim to increase its convergence rate and results' *** test result of runoff forecasting series shows that the precision of runoff forecasting is improved by means of the new method when the embedded dimension is high.
This paper introduced a new swarm based optimization technique for tuning Power System Stabilizer (PSS) that attached to a synchronous generator in a single machine infinite bus (SMIB) system. PSS which is installed w...
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ISBN:
(纸本)9781467350723
This paper introduced a new swarm based optimization technique for tuning Power System Stabilizer (PSS) that attached to a synchronous generator in a single machine infinite bus (SMIB) system. PSS which is installed with Lead-Lag (LL) controller is introduced to elevate the damping capability of the generator in the low frequency mode. For tuning three PSS-LL parameters, a new Particle Swarm Optimization (PSO) technique called Iteration PSO (IPSO) is proposed. In this method, a new iteration best index is implemented into conventional PSO in order to enhance the quality of the solution. Based on eigenvalues and damping ratio results, it is confirmed that the proposed technique is more efficient than conventional PSO in improving the angle stability of the system. Comparison between IPSO, PSO and evolutionary programming (EP) optimization techniques showed that the proposed computation approach give better solution and faster computation time.
Artificial Neural Networks (ANNs) have been applied to a variety of classification and learning tasks. The use of evolutionary Algorithms (EA) as one of the fastest, robust and efficient global search techniques has a...
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Artificial Neural Networks (ANNs) have been applied to a variety of classification and learning tasks. The use of evolutionary Algorithms (EA) as one of the fastest, robust and efficient global search techniques has allowed different properties of artificial neural networks to be evolved. This paper proposes the possibility' of using differential evolution for Determining an ANN Architecture (DNNA). We explain how to use differential evolution's application for determining an ANN architecture. The approach we describe is innovative and has only been successfully applied and implemented for the first time, although the idea of Differential Evolution has been applied in various fields since the last decade. In this work, we proposed an algorithm based on Differential Evolution that uses a minimum number of user specified parameters in determining an ANN architecture. By using back-propagation algorithm to train the ANN architecture partially during the evolution process, DNNA is evaluated on five benchmark classification problems, namely, Cancer, Diabetes, Heart Disease, Thyroid, and the Australian Credit Card problem. Through performance analysis and simulation studies, we show that DNNA can produce ANN architecture with good generalization abilities, but with less number of training cycles when compared with an evolutionary programming approach and standard back-propagation.
This paper presents the model of interleavers for the Interleave-Division Multiple-Access (IDMA) based on evolutionary algorithm. In all the previous works, interleavers are all generated independently and randomly wh...
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This paper presents the model of interleavers for the Interleave-Division Multiple-Access (IDMA) based on evolutionary algorithm. In all the previous works, interleavers are all generated independently and randomly which is simple but with good performance. Considered the difference between the model of interleavers and the traveling salesman problem(TSP), a specific fitness function based on covariance matrix is given and the optimum interleavers are computed by evolutionary algorithm. The simulation results show that the bit error ratio(BER) performance of the evolutionary interleavers(EI) is much better than other unrandom interleavers. The BER performance of independent and random interleavers is near to EI, it is a proof that EI is the theoretical optimum interleavers for IDMA.
The purpose of this research is to investigate the performance of heterogeneous multi-agent systems of agents in comparison to morphologically identical homogeneous systems, pertaining the same average physical and se...
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The purpose of this research is to investigate the performance of heterogeneous multi-agent systems of agents in comparison to morphologically identical homogeneous systems, pertaining the same average physical and sensory abilities for the system as a whole. We will be using a form of the well-known predator-prey pursuit problem to measure the efficiency of each of the systems in both speed of evolution of the exhibited behavior and robustness of the programmatically generated solutions.
The evolutionary neural network can be generated combining the evolutionary computation and neural network. Based on analysis of merits and demerits of previously proposed evolutionary neural network models, combining...
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The evolutionary neural network can be generated combining the evolutionary computation and neural network. Based on analysis of merits and demerits of previously proposed evolutionary neural network models, combining the immunized evolutionary programming proposed by author and BP neural network, a new evolutionary neural network model whose architecture and connection weights evolve simultaneously is proposed. At last, through the typical XOR problem, the new model is compared and analyzed with BP neural network and traditional evolutionary neural network. The computing results show that the precision and efficiency of the new model are all good.
A cluster analysis on a set of Retro-Transcribing viral proteomic sequences is described in this paper. A Lysine-Arginine concentration vector is calculated from the sequences and analyzed to identify correlations amo...
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ISBN:
(纸本)9781424452583
A cluster analysis on a set of Retro-Transcribing viral proteomic sequences is described in this paper. A Lysine-Arginine concentration vector is calculated from the sequences and analyzed to identify correlations among species. The computational strategy is based on the K-Means algorithm to partition the data into disjoint sets of points. A search method based on evolutionary programming is incorporated, in order to optimize the cluster structures. Experimental results show a number of interesting and unexpected similarities. These similarities could suggest bioelectronics relationships, in the context of the electronic mobility theory.
This paper addresses a multistage stochastic model for the optimal operation of wind farm, pumped storage and thermal power plants. The output of the wind farm and the electrical demand are considered as two independe...
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This paper addresses a multistage stochastic model for the optimal operation of wind farm, pumped storage and thermal power plants. The output of the wind farm and the electrical demand are considered as two independent stochastic processes. The evolution of these processes over time is modeled as a scenario tree. Considering all possible realizations of stochastic process, leads to a huge set of scenarios. These scenarios are reduced by a particle swarm optimization based scenario reduction algorithm. The scenario tree modeling transforms the cost model to a stochastic model. The stochastic model can be used to estimate the operation costs of the hybrid system under the influence of the uncertainties. The stochastic model is solved using adaptive particle swarm optimization.
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