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The design of robust networked structures is of significance in reality, and the integrity of network connections has been greatly emphasized in previous studies. However, besides structural integrity, a system should also keep the functionality when suffering from attacks and failures, i.e. robust community structure. Focusing on enhancing community robustness on complex networks, in this paper,...
Finding local community structure is an appealing problem that has attracted increasing attentions. Currently, it is unrealistic to get the complete information from graphs that are too large or evolve quickly. Moreover, in many real situations, we are only interested in the local community structure of networks, but not the whole network, because the local community structures can provide us much...
With the coming of big data age, the data usually present in a huge magnitude such as TB or more. These data contain both useful and useless information. Therefore, techniques which can effectively analyze these data are in urgent demand. In practice, dealing with Electroencephalographic (EEG) signals with Independent Component Analysis (ICA) approximates to a big optimization problem because it requires...
Based on texture features, we propose an unsupervised image classification method by using a novel evolutionary clustering technique, namely multiagent genetic clustering algorithm (MAGAc). In MAGAc, the clustering problem is considered from an optimization viewpoint. Each agent is a matrix of real numbers representing the cluster centers. Agents interact with others under the pressure of environment...
This paper analyzes the numerical optimization problems from the viewpoint of multiagent systems. First, Macro-Agent Evolutionary Model (MacroAEM) is proposed with the intrinsic properties of decomposable functions in mind. In this model, a subfunction forms a macro-agent, and 3 new behaviors, namely competition, cooperation, and selfishness, are developed for macro-agents to optimizing objective...
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