Abstract:

In multi-attribute complex large group-decision, effectively clustering the decision results of decision makers is the base to analyze and complete group decision. Aiming at the disadvantages for parameters selecting’s complexity, self-adaptive shortage and their randomicity in ants-clustering algorithm, an improved antsclustering algorithm is proposed. Several evaluating criterion values of a decision problem from decision makers are converted into preference vectors in the algorithm. The preference vectors’ clustered degree is taken as the calculating formula of the neighborhood similarity degree to form a heuristic clustering algorithm. A calculation example is used to show that the algorithm holds the characteristic of high clustering quality, self -organization and robustness, which is applicable to solve the problem for multiattribute complex large group clustering and decision making.