Abstract: Anomaly detection algorithm has played a key role in many areas, and the anomaly detection based on fuzzy C-means (FCM) is one of its representative methods. Owing to the limits of FCM such as the local minimum and the sensitiveness of the selection of initial value, there is still a large room to improve the conditional FCM-based anomaly detection method. In this paper, we firstly propose an adaptive artificial fish-swarm algorithm (AAFSA), by introducing an adaptive mechanism implemented by adjusting the value range of parameter “Visual” to the artificial fish-swarm algorithm which has a strong global search ability, to improve local and global optimization abilities and reduce the times of iterations. The limits of FCM mentioned above therefore can be solved by using the optimal solution obtained from AAFSA. Then, an anomaly detection algorithm based on AAFSA-FCM is designed by making full use of advantages of AAFSA to enhance the detection performances of anomaly detection algorithm. The experimental results show that the algorithm improves the detection performance both efficiently and effectively, which provides an effective solution for solving the problems of detection rate and false alarm rate in anomaly detection models, and state-of-the-art results achieve the purpose of reducing computational costs.