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This paper presents a differential evolution optimized fuzzy clustering algorithm (DEOFCA), which combines differential evolution (DE) algorithm and fuzzy clustering theory. Since DE algorithm has strong global search ability and good robustness, DEOFCA uses DE to replace the iteration process of fuzzy C means clustering algorithm, by which the global optimization capability is greatly improved. An...
In this paper, we introduce the parallel mechanism to the Differential Evolution (DE). Differential Evolution algorithm suffers from the problem that individuals gather in a single point and cannot escape the basin of a local optimum. We propose the parallel processing as a solution to this problem. Parallel processing helps DE maintain more than one best individual (attractor). Different attractors...
Clustering is a difficult problem, both with respect to the construction of adequate objective functions as well as to the optimization of the objective functions. In this article, the weighted sum validity function (WSVF) is improved as a dynamic weighted sum validity function(DWSVF) to evaluate fuzzy partitioning. Moreover, we proposed an adaptive differential evolution algorithm, which can be used...
A possibilistic clustering algorithm called unsupervised possibilistic clustering (UPC) was proposed in a previous paper. Although UPC is sound, the algorithm has the problem of generating coincident clusters. In this paper, we propose a new clustering model called improved unsupervised possibilistic clustering (IUPC) to overcome this weakness of UPC, and an efficient global optimization technique-differential...
In allusion to the problem of flood disaster classification, this paper proposes a modified differential evolution algorithm for dealing with a fuzzy clustering iterative model. By using variable index weight vector and penalty function, the objective function can be solved more perfectly. The new algorithm has been examined and tested on a practical flood disaster. The results show that the obtained...
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