First, the algorithm pseudocode: 1. Initialization: 1.1 point each to the two-dimensional space mapping pane, the point of each space is assigned a unique two-dimensional coordinates of the pane. A pane of only one point. 1.2 for each ant is assigned a unique address in the two-dimensional pane (the first step should be noted: the location and position of plane points pane space is completely different points, two points on the spatial position is calculated distance between two points and the position of the point on the plane is mainly used to determine the radius of the dot area S within the space of two points to calculate the distance, and then calculate the similarity group, and finally through group similarity calculating pickup probability plays or put down) 2, iterative tmax 3 times, once all the ants motion 4, R 5 a generates a random number between 0 and 1, if the current state of the ant is unloaded, and the current location at the little ant Oi 5.1 calculating the similar

20090322\ant_move.m
........\get_allneighbours.m
........\get_distance.m
........\get_Fi.m
........\get_Pd.m
........\get_Pp.m
........\has_item.m
........\initialize.m
........\test.m
........\一种利用信息熵的群体智能聚类算法.pdf
........\基于信息熵的蚁群聚类算法的改进.pdf
........\蚁群聚类程序文档.doc
20090322

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