Research Article

Core Business Selection Based on Ant Colony Clustering Algorithm

Pseudocode 1

(1) The number of individual ants is ; maximum number of iterations is ; side length of localized area
is ; velocity of ants, , takes a random number.
(2) The data object is projected onto a plane and each object is assigned a point ().
(3) Each ant is initialized to unload and randomly selects an object.
(4) for
                                 for
                     Calculate the average similarity of object.
                      If the antis unloaded, then calculate the probability of picking up, . If is greater than a random
            probability, while on the same time the object is not being picked up by other ants, the ant picks up the
            object and moves elsewhere randomly, and marks that it is load; otherwise, the ant refuses to pick up the
            object and selects other objects randomly.
            If the antis loaded, then calculate the probability of putting down, . If is greater than a random
            probability, the ant puts down the object and marks that it is unload, and then re-select a new
            object. Otherwise, the ant continues to move the object to a new location.
       End
End
(5) for
If an object is isolated or the number of its neighborhood is less than a certain constant, then mark the
object as an isolated point; otherwise, the object is assigned to a cluster serial number, and recursively their
neighborhood object tag for the same serial number.
      End