Mobile adaptive networks consist of a collection of nodes with learning and motion abilities that interact with each other locally in order to solve distributed processing and distributed inference problems in real-time. In this paper, we develop adaptation algorithms that exhibit self-organization properties and apply them to the model of cooperative hunting among predators. The results help provide an explanation for the agile adjustment of network patterns in the interaction between fish schools and predators.