Cluster and outlier detection method based on multi-agent evolution

The invention discloses a cluster and outlier detection method based on a multi-agent evolution, and mainly achieves that current traditional outlier detection algorithms can be used for detecting the outlier of high efficiency data cluster on data sets of different densities. The method comprises the steps of S1, initializing, S2, conducting K-means cluster algorithms to each intelligent agent, S3, calculating the energy of the intelligent agent, S4, performing a neighborhood competition operator, S5, performing a neighborhood crossover operator, S6, performing a mutation operator, S7, conducting K-means cluster algorithms, S8, conducting a self-learning operator, S9, updating a global optimization agent, S10, detecting the outlier, S11, obtaining a judgment result, S12, exporting outlier data, and S13, exporting data points with categories. The cluster and outlier detection method based on multi-agent evolution can effectively enhance the clustering efficiency and the outlier detection precision on different density data, reduce the calculation time, and be applicable to data sets of different densities.