Change detection in SAR images based on the salient map guidance and an accelerated genetic algorithm

This paper proposes a change detection algorithm in synthetic aperture radar (SAR) images based on the salient image guidance and an accelerated genetic algorithm (S-aGA). The difference image is first generated by logarithm ratio operator based on the bi-temporal SAR images acquired in the same region. Then a saliency detection model is applied in the difference image to extract the salient regions containing the changed class pixels. The salient regions are further divided by fuzzy c-means (FCM) clustering algorithm into three categories: changed class (set of pixels with high gray values), unchanged class (set of pixels with low gray values) and undetermined class (set of pixels with middle gray value, which are difficult to classify). Finally, the proposed accelerated GA is applied to explore the reduced search space formed by the undetermined-class pixels according to an objective function considering neighborhood information. In S-aGA, an efficient mutation operator is designed by using the neighborhood information of undetermined-class pixels as the heuristic information to determine the mutation probability of each undetermined-class pixel adaptively, which accelerates the convergence of the GA significantly. The experimental results on two data sets demonstrate the efficiency of the proposed S-aGA. On the whole, S-aGA outperforms five other existing methods including the simple GA in terms of detection accuracy. In addition, S-aGA could obtain satisfying solution within limited generations, converging much faster than the simple GA

Community mining using three closely joint techniques based on community mutual membership and refinement strategy

Community structure has become one of the central studies of the topological structure of complex networks in the past decades. Although many advanced approaches have been proposed to identify community structure, those state-of-the-art methods still lack efficiency in terms of a balance between stability, accuracy and computation time. Here, we propose an algorithm with different stages, called TJA-net, to efficiently identify communities in a large network with a good balance between accuracy, stability and computation time. First, we propose an initial labeling algorithm, called ILPA, combining K-nearest neighbor (KNN) and label propagation algorithm (LPA). To produce a number of sub-communities automatically, ILPA iteratively labels a node in a network using the labels of its adjacent nodes and their index of closeness. Next, we merge sub-communities using the mutual membership of two communities. Finally, a refinement strategy is designed for modifying the label of the wrongly clustered nodes at boundaries. In our approach, we propose and use modularity density as the objective function rather than the commonly used modularity. This can deal with the issue of the resolution limit for different network structures enhancing the result precision. We present a series of experiments with artificial and real data set and compare the results obtained by our proposed algorithm with the ones obtained by the state-of-the-art algorithms, which shows the effectiveness of our proposed approach. The experimental results on large-scale artificial networks and real networks illustrate the superiority of our algorithm

Differential Cloud Particles Evolution Algorithm Based on Data-Driven Mechanism for Applications of ANN

Computational scientists have designed many useful algorithms by exploring a biological process or imitating natural evolution. These algorithms can be used to solve engineering optimization problems. Inspired by the change of matter state, we proposed a novel optimization algorithm called differential cloud particles evolution algorithm based on data-driven mechanism (CPDD). In the proposed algorithm, the optimization process is divided into two stages, namely, fluid stage and solid stage. The algorithm carries out the strategy of integrating global exploration with local exploitation in fluid stage. Furthermore, local exploitation is carried out mainly in solid stage. The quality of the solution and the efficiency of the search are influenced greatly by the control parameters. Therefore, the data-driven mechanism is designed for obtaining better control parameters to ensure good performance on numerical benchmark problems. In order to verify the effectiveness of CPDD, numerical experiments are carried out on all the CEC2014 contest benchmark functions. Finally, two application problems of artificial neural network are examined. The experimental results show that CPDD is competitive with respect to other eight state-of-the-art intelligent optimization algorithms