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In social network, correlation estimation is a critical problem with promising application prospect. Numerical records of the interaction can serve as informative reflections of the correlation between users. However, due to the noise during data acquisition and storage as well as the privacy concern, the interaction data are usually partially observed. Moreover, even if the complete interaction is...
We propose a new population-based optimization algorithm, named Social Network-based Swarm Optimization algorithm (SNSO), for solving unconstrained single-objective optimization problems. In SNSO, the population topology, neighborhood structure and individual learning behavior are used to improve the search performance of a swarm. Specifically, a social network model is introduced to adjust the population...
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