Automatic Fuzzy Clustering Using Modified Differential Evolution for Image Classification

DEGSO: Hybrid Group Search Optimizer with Differential Evolution Operator

“…As in any other evolutionary algorithm, the NP vectors of initial population of DE is generated randomly, and then evaluated. The i th individual vector of the population at generation G has D components [12,13].…”

High-Resolution Satellite Imagery Changes Detection using Agglomerative Fuzzy K-Means Clustering Algorithm

“…Celik [5] employed c-means clustering and principal component analysis to perform change detection on multi temporal satellite imagery. Gosh et al [6] found that change detection of multi temporal satellite imagery using fuzzy c-means (FCM) and Gustafson-Kessel clustering algorithms produced better results than those obtained using Markov random field and other neural-networkbased algorithms. Clustering is a process of grouping a set of objects into clusters so that the objects in the same cluster have high similarity but are very dissimilar with objects in other clusters.…”

“…Various types of clustering methods have been proposed and developed; see, for instance, [1]. K-Means algorithm have been reported by Ruspini [4] and Bezdek [6], where each pattern is allowed to have memberships in all clusters rather than having a distinct membership to one single cluster. Numerous problems in real world applications, such as pattern recognition and computer vision, can be tackled effectively by the fuzzy K-Means algorithms, see, for instance, [7], [8], and [9].…”

An Adaptive Masker for the Differential Evolution Algorithm

“…In [13], Das et al proposed automatic crisp clustering using improved DE for the image segmentation application. A Modified Differential Evolution algorithm (MoDE) is proposed in [9], in this article three vectors are used in the mutation process, the global best, the local best and a randomly selected vector. This modified mutation process is governed by an adaptive parameter.…”

“…In order to increase the exploration and exploitation capabilities during the evolution process, the maskers matrix zones widths are governed by adaptive parameters. In order to show the efficiency of the proposed algorithm, a comparative study has been done with other fuzzy clustering algorithms, iterative fuzzy c-means (IFCM) [11], improved differential evolution (AFCIDE) [13], variable length genetic algorithm (VGAFC) [14] and a Modified Differential Evolution algorithm (MoDEAFC) [9]. Clustering results are reported for a numeric remote sensing data.…”