This paper presents a novel optimization approach to constrained economic load dispatch (ELD) problem using artificial immune system (AIS). The approach utilizes the clonalselection principle and evolutionary approac...
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This paper presents a novel optimization approach to constrained economic load dispatch (ELD) problem using artificial immune system (AIS). The approach utilizes the clonalselection principle and evolutionary approach wherein cloning of antibodies is performed followed by hypermutation. The proposed methodology easily takes care of transmission losses, dynamic operation constraints (ramp rate limits) and prohibited zones and also accounts for non-smoothness of cost function arising due to the use of multiple fuels. Simulations were performed over various systems with different number of generating units and comparisons are performed with other prevalent approaches. The findings affirmed the robustness, fast convergence and proficiency of proposed methodology over other existing techniques. (c) 2006 Elsevier B.V. All rights reserved.
Image segmentation is the prerequisite step for further image analysis. Segmentation algorithms based on clustering attract more and more attentions. In this paper, an image-domain based clustering method for segmenta...
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ISBN:
(纸本)9780819469502
Image segmentation is the prerequisite step for further image analysis. Segmentation algorithms based on clustering attract more and more attentions. In this paper, an image-domain based clustering method for segmentation, called CSA-CA, is proposed. In this method, a scale parameter is introduced instead of an apriori known number of clusters. Considering that adjacent pixels are generally not independent of each other, the spatial local context is took account into our method. A spatial information term is added so that the near pixels have higher probability to merge into one cluster. Additionally, a clonalselection clustering operator is used so that a cluster is capable of exploring the others that are not neighboring in spatial but similar in feature. In the experiments we show the effectiveness of the proposed method and compare it to other segmentation algorithms.
The application of artificial immunity and fuzzy kernel clustering in data classification is studied, and a new hybrid clustering algorithm incorporating artificial immunity into fuzzy kernel clustering for pattern re...
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ISBN:
(纸本)9787811240559
The application of artificial immunity and fuzzy kernel clustering in data classification is studied, and a new hybrid clustering algorithm incorporating artificial immunity into fuzzy kernel clustering for pattern recognition is proposed in this paper. The algorithm, by combining kernel-based fuzzy clustering with artificial immune evolution algorithm, which learns from the mechanism of immunocyte clone, memory and affinity maturation in natural immune system, operates on antibody with clone, hyper-mutation and restraint in each generation. The algorithm can quickly obtain global optima, and perfectly solve the flaws of the fuzzy c-means and kernel clustering algorithm, which are sensitive to initialization and easy to involve local optima. Our experiments on IRIS data as well as compressor fault data demonstrate the feasibility and effectiveness of the new algorithm.
In this study we explore the feasibility of applying Artificial Neural Networks (ANN) and Support Vector Machines (SVM) to the prediction of incipient power transformer faults. A clonal selection algorithm (CSA) is in...
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ISBN:
(纸本)354045764X
In this study we explore the feasibility of applying Artificial Neural Networks (ANN) and Support Vector Machines (SVM) to the prediction of incipient power transformer faults. A clonal selection algorithm (CSA) is introduced for the first time in the literature to select optimal input features and RBF kernel parameters. CSA is shown to be capable of improving the speed and accuracy of classification systems by removing redundant and potentially confusing input features, and of optimizing the kernel parameters simultaneously. Simulation results on practice data demonstrate the effectiveness and high efficiency of the proposed approach.
In the field of cluster analysis, objective function based clustering algorithm is one of widely applied methods. However, this type of algorithms need the priori knowledge about the cluster number and the form of clu...
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ISBN:
(纸本)0780384849
In the field of cluster analysis, objective function based clustering algorithm is one of widely applied methods. However, this type of algorithms need the priori knowledge about the cluster number and the form of clustering prototypes, which can only process data sets with the same type of prototypes. Moreover, these algorithms are very sensitive to the initialization and easy to get trap into local optima. For the purpose, this paper presents a novel clustering method with network structure based on clonalalgorithm to realize the automatization of cluster analysis. By analyzing the neurons of the obtained network with minimal spanning tree, one can easily get the cluster number and the related classification information. The test results with various data sets illustrate that the novel algorithm achieves more effective performance on cluster analyzing the data set with mixed numeric values and categorical values.
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