In complicated social networks, discovering or predicting important events is significant. The theoretical framework and evaluation methods of link prediction offer an effective solution for detecting events in social networks. Most of the current research focuses on proposing different similarity indexes to achieve higherlink prediction accuracy. However this type of approach has following problems:(1) Because different similarity indexes are designed for different networks, they are not universal; (2) The independent similarity index is difficult to reflect diversity and complexity of real network evolutions; (3) Without considering the fluctuation in the network evolution, the link prediction cannot detect events. To solve these problems, this paper proposes a swarm intelligence method based on mixed indexes (IndexEvent), which can evaluate fluctuations and detect events in social networks. The main work is as follow:(1) A proof is provided on the proposed mixed indexes that the link prediction algorithm based on mixed indexes can achieve a higher accuracy; (2) Based on the quantum-behaved particle swarm algorithm, an optimal weight algorithm (OWA) is developed to determine best mixed indexes for different networks efficiently; (3) A fluctuation detection algorithm (FDA) is designed to quantitatively estimates fluctuations in network evolutions at different periods. And micro factors are taken into account to improve FDA. The results of the experiments show that IndexEvent can effectively reflect evolution fluctuations and detect events.