Research on the Optimal Aggregation Method of Decision Maker Preference Judgment Matrix for Group Decision Making

The main focus of this paper is to present a new aggregation method of judgment matrices, which is based on the optimal aggregation model and the efficient aggregation algorithm. The reciprocal elements in the decision maker judgment matrices are mapped into the corresponding points on the two-dimensional coordinates. We can express the differences between different decision makers' preferences by the Euclidean distance among these points. We use the plant growth simulation algorithm (PGSA) to obtain the optimal aggregation points which can reflect the opinions of the entire decision makers group. The aggregation matrix of decision maker preference is composed of these optimal aggregation points and the consistency test has been passed. Compared with the weighted geometric mean method (WGMM) and minimum distance method (MDM), the sum of Euclidean distances from the aggregation points to other given points in this paper is minimal. The validity and rationality of this method are also verified by the analysis and comparison of examples, which provides a new idea to solve the group decision making (GDM) problems.