The non-dominated sorting geneticalgorithm III (NSGA-III) has recently been proposed to solve many-objective optimization problems (MaOPs). While this algorithm achieves good diversity, its convergence is unsatisfact...
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The non-dominated sorting geneticalgorithm III (NSGA-III) has recently been proposed to solve many-objective optimization problems (MaOPs). While this algorithm achieves good diversity, its convergence is unsatisfactory. In order to improve the convergence, we propose an improved NSGA-III using a genetic k-means clustering algorithm (NSGA-III-GkM), which can also ensure diversity and automatically provide the number and direction vector of the subspaces. Compared with the NSGA-III, the proposed NSGA-III-GkM has two key features. First, the initial reference points are clustered using a GkM clusteringalgorithm, which realizes automatic learning of the number of clusters. Second, as the reference points are replaced by cluster centers, a penalty-based boundary intersection (PBI) aggregation function is introduced to replace the perpendicular distance. The proposed NSGA-III-GkM and other similar optimization algorithms (NSGA-III, MOEA/D, U-NSGA-III, DC-NSGA-III and B-NSGA-III) are tested on DTLZ test problems and OF test problems. The simulation results demonstrate that the NSGA-III-GkM exhibits better diversity and convergence performance than the other algorithms.
Due to the complicated characteristics of regional geochemical data from stream sediments as a result of the complexity of geological features, detection of multi-elemental geochemical footprints of mineral deposits o...
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Due to the complicated characteristics of regional geochemical data from stream sediments as a result of the complexity of geological features, detection of multi-elemental geochemical footprints of mineral deposits of interest is a challenging task. As a way to address this, a hybrid geneticalgorithm-based technique, namely genetick-meansclustering (GkMC) algorithm, is proposed here for optimum delineation of multi-elemental patterns (both anomaly and background) in stream sediment geochemical data. To do so, factor analysis and sample catchment basin modeling were coupled with GkMC and traditional k-meansclustering (TkMC) methods for identification of anomalous multi-elemental geochemical footprints of deposits of porphyry copper and skarn copper in the 1:100,000 scale Varzaghan map sheet, northwest Iran. Based on higher prediction rate, it can be inferred that the model derived by GkMC is superior to the one derived by TkMC. In addition, the strong anomaly classes of the GkMC and TkMC models predict, respectively, similar to 83% and similar to 66% of the porphyry/skam Cu deposits in similar to 22% and similar to 36% of the study district. Thus, the geochemical anomaly targets derived by the GkMC method are more reliable than those generated by the TkMC method. This revealed that the GkMC algorithm is an efficient and robust tool for recognizing multi-element geochemical anomalies for mineral exploration.
An improved genetic k-means clustering algorithm is proposed and is applied to image segmentation. According to the characteristics of the image, the feature vector of the pixel is properly chosen and the weight facto...
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ISBN:
(纸本)9780769535579
An improved genetic k-means clustering algorithm is proposed and is applied to image segmentation. According to the characteristics of the image, the feature vector of the pixel is properly chosen and the weight factors of the feature vector are adjusted, which enhances the segmentation precision. The selection of conventional geneticalgorithm and the modification of mutation operations improve the speed of convergence. Computing time is reduced due to combining the membership matrix with the coding of chromosomes skillfully. The results of the experiments demonstrate that in the image segmentation the proposed algorithm is better than traditional genetick-meansalgorithm.
Evaluating a given clustering result is a very difficult problem in real world. Cluster validity indices are developed for this purpose. There are two different types of cluster validity indices available : External a...
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ISBN:
(纸本)9781467351157;9781467351164
Evaluating a given clustering result is a very difficult problem in real world. Cluster validity indices are developed for this purpose. There are two different types of cluster validity indices available : External and Internal. External cluster validity indices utilize some supervised information and internal cluster validity indices utilize the intrinsic structure of the data. In this paper a new external cluster validity index, MMI has been implemented based on Max-Min distance among data points and prior information based on structure of the data. A new probabilistic approach has been implemented to find the correct correspondence between the true and obtained clustering. genetick-meansalgorithm (GAk-means) and single linkage have been used as the underlying clustering techniques. Results of the proposed index for identifying the appropriate number of clusters is shown for five artificial and two real-life data sets. GAk-means and single linkage clustering techniques are used as the underlying partitioning techniques with the number of clusters varied over a range. The MMI index is then used to determine the appropriate number of clusters. The performance of MMI is compared with existing external cluster validity indices, adjusted rand index (ARI) and rand index (RI). It works well for two class and multi class data sets.
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