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In general, distributed algorithms for this problem are likely to suffer from the failures that localizations are trapped in local minima. Focusing on this issue, this article considers a fully distributed algorithm by introducing a novel mechanism, where each individual node is allowed to computationally interact with a random subset of its neighbors, for helping localizations escape from local minima. Theoretical analyses reveal that with the proposed algorithm, any local minimum of the localization will be unstable, and the global optimum would finally be achieved with probability 1 after enough time of iterations. 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