Self-adaptive FCM and MMAS for police patrols

摘要

Through researching and analyzing regions and routes in police patrols, we use self-adaptive fuzzy C-means clustering algorithm, dijkstra algorithm and self-adaptive max-min ant system (MMAS) to solve the problem of police patrols. We put self-adaptive strategy and fuzzy C-means (FCM) clustering algorithm together to form a self-adaptive FCM clustering algorithm. It is a good solution to the problem of local optimum as well as sensitivity to the initial value for the traditional FCM clustering algorithm. In the experiment, it is used in the regional division of police patrols in a city, and it has been proved in the division of the region that the sum of distance between a police vehicle and each possible accident scene can achieve the minimum value, which shows a significant effect of police patrols. And through the improved dijkstra algorithm to calculate shortest path length between a police vehicle and an accident scene, it proves that a police vehicle in the division of the region arrives at an accident scene within three minutes after accepting the warnings, whose proportion is 90.2%. Finally, we put parameter adaptive thinking and MMAS together to form self-adaptive MMAS, which is used to calculate optimal patrol circuit in the division of the region. Experiments show that the problem is well solved.